- Book
- 534 p. : ill. ; 25 cm.
- A catastrophic failure of prediction
- Are you smarter than a television pundit?
- All I care about is W's and L's
- For years you've been telling us that rain is green
- Desperately seeking signal
- How to drown in three feet of water
- Role models
- Less and less and less wrong
- Rage against the machines
- The poker bubble
- If you can't beat 'em--
- A climate of healthy skepticism
- What you don't know can hurt you.
- A catastrophic failure of prediction
- Are you smarter than a television pundit?
- All I care about is W's and L's
- For years you've been telling us that rain is green
- Desperately seeking signal
- How to drown in three feet of water
- Role models
- Less and less and less wrong
- Rage against the machines
- The poker bubble
- If you can't beat 'em--
- A climate of healthy skepticism
- What you don't know can hurt you.
Law Library (Crown)
Law Library (Crown) | Status |
---|---|
Basement | |
CB158 .S54 2012 | Unknown |
CB158 .S54 2012 | Unknown |
CB158 .S54 2012 | Unknown |
On reserve: Ask at circulation desk | |
CB158 .S54 2012 | Unknown 2-hour loan |
CB158 .S54 2012 | Unknown 2-hour loan |
LAW-243-01
- Course
- LAW-243-01 -- Bayesian Statistics and Econometrics
- Instructor(s)
- Strnad, James Frank
2. Introduction to econometrics [2011]
- Book
- xlii, 785 p. : ill. ; 24 cm.
- Economic questions and data
- Review of probability
- Review of statistics
- Linear regression with one regressor
- Regression with a single regressor : hypothesis tests and confidence intervals
- Linear regression with multiple regressors
- Hypothesis tests and confidence intervals in multiple regression
- Nonlinear regression functions
- Assessing studies based on multiple regression
- Regression with panel data
- Regression with a binary dependent variable
- Instrumental variables regression
- Experiments and quasi-experiments
- Introduction to time series regression and forecasting
- Estimation of dynamic causal effects
- Additional topics in time series regression
- The theory of linear regression with one regressor
- The theory of multiple regression.
(source: Nielsen Book Data)9781408264331 20160605
- Economic questions and data
- Review of probability
- Review of statistics
- Linear regression with one regressor
- Regression with a single regressor : hypothesis tests and confidence intervals
- Linear regression with multiple regressors
- Hypothesis tests and confidence intervals in multiple regression
- Nonlinear regression functions
- Assessing studies based on multiple regression
- Regression with panel data
- Regression with a binary dependent variable
- Instrumental variables regression
- Experiments and quasi-experiments
- Introduction to time series regression and forecasting
- Estimation of dynamic causal effects
- Additional topics in time series regression
- The theory of linear regression with one regressor
- The theory of multiple regression.
(source: Nielsen Book Data)9781408264331 20160605
Law Library (Crown)
Law Library (Crown) | Status |
---|---|
On reserve: Ask at circulation desk | |
HB139 .S765 2011 | Unknown 2-hour loan |
HB139 .S765 2011 | Unknown 2-hour loan |
HB139 .S765 2011 | Unknown 2-hour loan |
LAW-243-01, LAW-7512-01
- Course
- LAW-243-01 -- Bayesian Statistics and Econometrics
- Instructor(s)
- Strnad, James Frank
- Course
- LAW-7512-01 -- Statistical Inference in Law
- Instructor(s)
- Ho, Daniel E.
3. Basic econometrics [2009]
- Book
- xx, 922 p. : ill. ; 26 cm.
- Part I: Single-Equation Regression Model Chapter 1: The Nature of Regression Analysis Chapter 2: Two-Variable Regression Analysis: Some Basic Ideas Chapter 3: Two Variable Regression Model: The Problem of Estimation Chapter 4: Classical Normal Linear Regression Model (CNLRM) Chapter 5: Two-Variable Regression: Interval Estimation and Hypothesis Testing Chapter 6: Extensions of the Two-Variable Linear Regression Model Chapter 7: Multiple Regression Analysis: The Problem of Estimation Chapter 8: Multiple Regression Analysis: The Problem of Inference Chapter 9: Dummy Variable Regression Models Part II: Relaxing the Assumptions of the Classical Model Chapter 10: Multicollinearity: What happens if the Regressor are Correlated Chapter 11: Heteroscedasticity: What Happens if the Error Variance is Nonconstant? Chapter 12: Autocorrelation: What Happens if the Error Terms are Correlated Chapter 13: Econometric Modeling: Model Specification and Diagnostic Testing Part III: Topics in Econometrics Chapter 14: Nonlinear Regression Models Chapter 15: Qualitative Response Regression Models Chapter 16: Panel Data Regression Models Chapter 17: Dynamic Econometric Model: Autoregressive and Distributed-Lag Models. Part IV: Simultaneous-Equation Models Chapter 18: Simultaneous-Equation Models. Chapter 19: The Identification Problem. Chapter 20: Simultaneous-Equation Methods. Chapter 21: Time Series Econometrics: Some Basic Concepts Chapter 22: Time Series Econometrics: Forecasting Appendix A: Review of Some Statistical Concepts Appendix B: Rudiments of Matrix Algebra Appendix C: The Matrix Approach to Linear Regression Model Appendix D: Statistical Tables Appendix E: Computer Output of EViews, MINITAB, Excel, and STATA Appendix F: Economic Data on the World Wide Web.
- (source: Nielsen Book Data)9780071276252 20160528
- Part I: Single-Equation Regression Model 1: The Nature of Regression Analysis 2: Two-Variable Regression Analysis: Some Basic Ideas 3: Two Variable Regression Model: The Problem of Estimation 4: Classical Normal Linear Regression Model (CNLRM) 5: Two-Variable Regression: Interval Estimation and Hypothesis Testing 6: Extensions of the Two-Variable Linear Regression Model 7: Multiple Regression Analysis: The Problem of Estimation 8: Multiple Regression Analysis: The Problem of Inference 9: Dummy Variable Regression Models Part II: Relaxing the Assumptions of the Classical Model 10: Multicollinearity: What happens if the Regressor are Correlated 11: Heteroscedasticity: What Happens if the Error Variance is Nonconstant? 12: Autocorrelation: What Happens if the Error Terms are Correlated Chapter 13: Econometric Modeling: Model Specification and Diagnostic Testing Part III: Topics in Econometrics 14: Nonlinear Regression Models 15: Qualitative Response Regression Models 16: Panel Data Regression Models 17: Dynamic Econometric Model: Autoregressive and Distributed-Lag Models. Part IV: Simultaneous-Equation Models 18: Simultaneous-Equation Models. 19: The Identification Problem. 20: Simultaneous-Equation Methods. 21: Time Series Econometrics: Some Basic Concepts 22: Time Series Econometrics: Forecasting Appendix A: Review of Some Statistical Concepts Appendix B: Rudiments of Matrix Algebra Appendix C: The Matrix Approach to Linear Regression Model Appendix D: Statistical Tables Appendix E: Computer Output of EViews, MINITAB, Excel, and STATA Appendix F: Economic Data on the World Wide Web.
- (source: Nielsen Book Data)9780073375779 20160528
- Part I: Single-Equation Regression Model Chapter 1: The Nature of Regression Analysis Chapter 2: Two-Variable Regression Analysis: Some Basic Ideas Chapter 3: Two Variable Regression Model: The Problem of Estimation Chapter 4: Classical Normal Linear Regression Model (CNLRM) Chapter 5: Two-Variable Regression: Interval Estimation and Hypothesis Testing Chapter 6: Extensions of the Two-Variable Linear Regression Model Chapter 7: Multiple Regression Analysis: The Problem of Estimation Chapter 8: Multiple Regression Analysis: The Problem of Inference Chapter 9: Dummy Variable Regression Models Part II: Relaxing the Assumptions of the Classical Model Chapter 10: Multicollinearity: What happens if the Regressor are Correlated Chapter 11: Heteroscedasticity: What Happens if the Error Variance is Nonconstant? Chapter 12: Autocorrelation: What Happens if the Error Terms are Correlated Chapter 13: Econometric Modeling: Model Specification and Diagnostic Testing Part III: Topics in Econometrics Chapter 14: Nonlinear Regression Models Chapter 15: Qualitative Response Regression Models Chapter 16: Panel Data Regression Models Chapter 17: Dynamic Econometric Model: Autoregressive and Distributed-Lag Models. Part IV: Simultaneous-Equation Models Chapter 18: Simultaneous-Equation Models. Chapter 19: The Identification Problem. Chapter 20: Simultaneous-Equation Methods. Chapter 21: Time Series Econometrics: Some Basic Concepts Chapter 22: Time Series Econometrics: Forecasting Appendix A: Review of Some Statistical Concepts Appendix B: Rudiments of Matrix Algebra Appendix C: The Matrix Approach to Linear Regression Model Appendix D: Statistical Tables Appendix E: Computer Output of EViews, MINITAB, Excel, and STATA Appendix F: Economic Data on the World Wide Web.
- (source: Nielsen Book Data)9780071276252 20160528
- Part I: Single-Equation Regression Model 1: The Nature of Regression Analysis 2: Two-Variable Regression Analysis: Some Basic Ideas 3: Two Variable Regression Model: The Problem of Estimation 4: Classical Normal Linear Regression Model (CNLRM) 5: Two-Variable Regression: Interval Estimation and Hypothesis Testing 6: Extensions of the Two-Variable Linear Regression Model 7: Multiple Regression Analysis: The Problem of Estimation 8: Multiple Regression Analysis: The Problem of Inference 9: Dummy Variable Regression Models Part II: Relaxing the Assumptions of the Classical Model 10: Multicollinearity: What happens if the Regressor are Correlated 11: Heteroscedasticity: What Happens if the Error Variance is Nonconstant? 12: Autocorrelation: What Happens if the Error Terms are Correlated Chapter 13: Econometric Modeling: Model Specification and Diagnostic Testing Part III: Topics in Econometrics 14: Nonlinear Regression Models 15: Qualitative Response Regression Models 16: Panel Data Regression Models 17: Dynamic Econometric Model: Autoregressive and Distributed-Lag Models. Part IV: Simultaneous-Equation Models 18: Simultaneous-Equation Models. 19: The Identification Problem. 20: Simultaneous-Equation Methods. 21: Time Series Econometrics: Some Basic Concepts 22: Time Series Econometrics: Forecasting Appendix A: Review of Some Statistical Concepts Appendix B: Rudiments of Matrix Algebra Appendix C: The Matrix Approach to Linear Regression Model Appendix D: Statistical Tables Appendix E: Computer Output of EViews, MINITAB, Excel, and STATA Appendix F: Economic Data on the World Wide Web.
- (source: Nielsen Book Data)9780073375779 20160528
Law Library (Crown)
Law Library (Crown) | Status |
---|---|
On reserve: Ask at circulation desk | |
HB139 .G84 2009 | Unknown 2-hour loan |
HB139 .G84 2009 | Unknown 2-hour loan |
HB139 .G84 2009 | Unknown 2-hour loan |
LAW-243-01
- Course
- LAW-243-01 -- Bayesian Statistics and Econometrics
- Instructor(s)
- Strnad, James Frank
- Book
- xx, 865 p. : ill. ; 25 cm.
- Regression analysis with cross-sectional data
- Regression analysis with time series data
- Advanced topics.
(source: Nielsen Book Data)9780324581621 20160604
- Regression analysis with cross-sectional data
- Regression analysis with time series data
- Advanced topics.
(source: Nielsen Book Data)9780324581621 20160604
Law Library (Crown)
Law Library (Crown) | Status |
---|---|
On reserve: Ask at circulation desk | |
HB139 .W665 2009 | Unknown 2-hour loan |
LAW-243-01
- Course
- LAW-243-01 -- Bayesian Statistics and Econometrics
- Instructor(s)
- Strnad, James Frank
5. A guide to econometrics [2008]
- Book
- xii, 585 p. : ill. ; 26 cm.
- Preface.Dedication.1. Introduction.1.1 What is Econometrics?.1.2 The Disturbance Term.1.3 Estimates and Estimators.1.4 Good and Preferred Estimators.General Notes.Technical Notes.2. Criteria for Estimators.2.1 Introduction.2.2 Computational Cost.2.3 Least Squares.2.4 Highest R2.2.5 Unbiasedness.2.6 Efficiency.2.7 Mean Square Error (MSE).2.8 Asymptotic Properties.2.9 Maximum Likelihood.2.10 Monte Carlo Studies.2.11 Adding Up.General Notes.Technical Notes.3. The Classical Linear Regression Model.3.1 Textbooks as Catalogs.3.2 The Five Assumptions.3.3 The OLS Estimator in the CLR Model.General Notes.Technical Notes.4. Interval Estimation and Hypothesis Testing.4.1 Introduction.4.2 Testing a Single Hypothesis: the t Test.4.3 Testing a Joint Hypothesis: the F Test.4.4 Interval Estimation for a Parameter Vector.4.5 LR, W, and LM Statistics.4.6 Bootstrapping.General Notes.Technical Notes.5. Specification.5.1 Introduction.5.2 Three Methodologies.5.3 General Principles for Specification.5.4 Misspecification Tests/Diagnostics.5.5 R2 Again.General Notes.Technical Notes.6. Violating Assumption One: Wrong Regressors, Nonlinearities, and Parameter Inconstancy.6.1 Introduction.6.2 Incorrect Set of Independent Variables.6.3 Nonlinearity.6.4 Changing Parameter Values.General Notes.Technical Notes.7. Violating Assumption Two: Nonzero Expected Disturbance.General Notes.8. Violating Assumption Three: Nonspherical Disturbances.8.1 Introduction.8.2 Consequences of Violation.8.3 Heteroskedasticity.8.4 Autocorrelated Disturbances.8.5 Generalized Method of Moments.General Notes.Technical Notes.9. Violating Assumption Four: Instrumental Variable Estimation.9.1 Introduction.9.2 The IV Estimator.9.3 IV Issues.General Notes.Technical Notes.10. Violating Assumption Four: Measurement Errors and Autoregression.10.1 Errors in Variables.10.2 Autoregression.General Notes.Technical Notes.11. Violating Assumption Four: Simultaneous Equations.11.1 Introduction.11.2 Identification.11.3 Single-equation Methods.11.4 Systems Methods.General Notes.Technical Notes.12. Violating Assumption Five: Multicollinearity.12.1 Introduction.12.2 Consequences.12.3 Detecting Multicollinearity.12.4 What to Do.General Notes.Technical Notes.13. Incorporating Extraneous Information.13.1 Introduction.13.2 Exact Restrictions.13.3 Stochastic Restrictions.13.4 Pre-test Estimators.13.5 Extraneous Information and MSE.General Notes.Technical Notes.14. The Bayesian Approach.14.1 Introduction.14.2 What Is a Bayesian Analysis?.14.3 Advantages of the Bayesian Approach.14.4 Overcoming Practitioners' Complaints.General Notes.Technical Notes.15. Dummy Variables.15.1 Introduction.15.2 Interpretation.15.3 Adding Another Qualitative Variable.15.4 Interacting with Quantitative Variables.15.5 Observation-specific Dummies.General Notes.Technical Notes.16. Qualitative Dependent Variables.16.1 Dichotomous Dependent Variables.16.2 Polychotomous Dependent Variables.16.3 Ordered Logit/Probit.16.4 Count Data.General Notes.Technical Notes.17. Limited Dependent Variables.17.1 Introduction.17.2 The Tobit Model.17.3 Sample Selection.17.4 Duration Models.General Notes.Technical Notes.18. Panel Data.18.1 Introduction.18.2 Allowing for Different Intercepts.18.3 Fixed versus Random Effects.18.4 Short Run versus Long Run.18.5 Long, Narrow Panels.General Notes.Technical Notes.19. Time Series Econometrics.19.1 Introduction.19.2 ARIMA Models.19.3 VARs.19.4 Error-correction Models.19.5 Testing for Unit Roots.19.6 Cointegration.General Notes.Technical Notes.20. Forecasting.20.1 Introduction.20.2 Causal Forecasting/Econometric Models.20.3 Time Series Analysis.20.4 Forecasting Accuracy.General Notes.Technical Notes.21. Robust Estimation.21.1 Introduction.21.2 Outliers and Influential Observations.21.3 Guarding Against Influential Observations.21.4 Artificial Neural Networks.21.5 Non-parametric Estimation.General Notes.Technical Notes.22. Applied Econometrics.22.1 Introduction.22.2 The Ten Commandments of Applied.Econometrics.22.3 Getting the Wrong Sign.22.4 Common Mistakes.22.5 What Do Practitioners Need to Know?.General Notes.Technical Notes.23. Computational Considerations.23.1 Introduction.23.2 Optimizing via a Computer Search.23.3 Estimating Integrals via Simulation.23.4 Drawing Observations from Awkward Distributions.General Notes.Technical Notes.Appendix A: Sampling Distributions, the.Foundation of Statistics.Appendix B: All about Variance.Appendix C: A Primer on Asymptotics.Appendix D: Exercises.Appendix E: Answers to Even-numbered Questions.Glossary.Bibliography.Name Index.Subject Index.
- (source: Nielsen Book Data)9781405182584 20160528
(source: Nielsen Book Data)9781405182584 20160528
- Preface.Dedication.1. Introduction.1.1 What is Econometrics?.1.2 The Disturbance Term.1.3 Estimates and Estimators.1.4 Good and Preferred Estimators.General Notes.Technical Notes.2. Criteria for Estimators.2.1 Introduction.2.2 Computational Cost.2.3 Least Squares.2.4 Highest R2.2.5 Unbiasedness.2.6 Efficiency.2.7 Mean Square Error (MSE).2.8 Asymptotic Properties.2.9 Maximum Likelihood.2.10 Monte Carlo Studies.2.11 Adding Up.General Notes.Technical Notes.3. The Classical Linear Regression Model.3.1 Textbooks as Catalogs.3.2 The Five Assumptions.3.3 The OLS Estimator in the CLR Model.General Notes.Technical Notes.4. Interval Estimation and Hypothesis Testing.4.1 Introduction.4.2 Testing a Single Hypothesis: the t Test.4.3 Testing a Joint Hypothesis: the F Test.4.4 Interval Estimation for a Parameter Vector.4.5 LR, W, and LM Statistics.4.6 Bootstrapping.General Notes.Technical Notes.5. Specification.5.1 Introduction.5.2 Three Methodologies.5.3 General Principles for Specification.5.4 Misspecification Tests/Diagnostics.5.5 R2 Again.General Notes.Technical Notes.6. Violating Assumption One: Wrong Regressors, Nonlinearities, and Parameter Inconstancy.6.1 Introduction.6.2 Incorrect Set of Independent Variables.6.3 Nonlinearity.6.4 Changing Parameter Values.General Notes.Technical Notes.7. Violating Assumption Two: Nonzero Expected Disturbance.General Notes.8. Violating Assumption Three: Nonspherical Disturbances.8.1 Introduction.8.2 Consequences of Violation.8.3 Heteroskedasticity.8.4 Autocorrelated Disturbances.8.5 Generalized Method of Moments.General Notes.Technical Notes.9. Violating Assumption Four: Instrumental Variable Estimation.9.1 Introduction.9.2 The IV Estimator.9.3 IV Issues.General Notes.Technical Notes.10. Violating Assumption Four: Measurement Errors and Autoregression.10.1 Errors in Variables.10.2 Autoregression.General Notes.Technical Notes.11. Violating Assumption Four: Simultaneous Equations.11.1 Introduction.11.2 Identification.11.3 Single-equation Methods.11.4 Systems Methods.General Notes.Technical Notes.12. Violating Assumption Five: Multicollinearity.12.1 Introduction.12.2 Consequences.12.3 Detecting Multicollinearity.12.4 What to Do.General Notes.Technical Notes.13. Incorporating Extraneous Information.13.1 Introduction.13.2 Exact Restrictions.13.3 Stochastic Restrictions.13.4 Pre-test Estimators.13.5 Extraneous Information and MSE.General Notes.Technical Notes.14. The Bayesian Approach.14.1 Introduction.14.2 What Is a Bayesian Analysis?.14.3 Advantages of the Bayesian Approach.14.4 Overcoming Practitioners' Complaints.General Notes.Technical Notes.15. Dummy Variables.15.1 Introduction.15.2 Interpretation.15.3 Adding Another Qualitative Variable.15.4 Interacting with Quantitative Variables.15.5 Observation-specific Dummies.General Notes.Technical Notes.16. Qualitative Dependent Variables.16.1 Dichotomous Dependent Variables.16.2 Polychotomous Dependent Variables.16.3 Ordered Logit/Probit.16.4 Count Data.General Notes.Technical Notes.17. Limited Dependent Variables.17.1 Introduction.17.2 The Tobit Model.17.3 Sample Selection.17.4 Duration Models.General Notes.Technical Notes.18. Panel Data.18.1 Introduction.18.2 Allowing for Different Intercepts.18.3 Fixed versus Random Effects.18.4 Short Run versus Long Run.18.5 Long, Narrow Panels.General Notes.Technical Notes.19. Time Series Econometrics.19.1 Introduction.19.2 ARIMA Models.19.3 VARs.19.4 Error-correction Models.19.5 Testing for Unit Roots.19.6 Cointegration.General Notes.Technical Notes.20. Forecasting.20.1 Introduction.20.2 Causal Forecasting/Econometric Models.20.3 Time Series Analysis.20.4 Forecasting Accuracy.General Notes.Technical Notes.21. Robust Estimation.21.1 Introduction.21.2 Outliers and Influential Observations.21.3 Guarding Against Influential Observations.21.4 Artificial Neural Networks.21.5 Non-parametric Estimation.General Notes.Technical Notes.22. Applied Econometrics.22.1 Introduction.22.2 The Ten Commandments of Applied.Econometrics.22.3 Getting the Wrong Sign.22.4 Common Mistakes.22.5 What Do Practitioners Need to Know?.General Notes.Technical Notes.23. Computational Considerations.23.1 Introduction.23.2 Optimizing via a Computer Search.23.3 Estimating Integrals via Simulation.23.4 Drawing Observations from Awkward Distributions.General Notes.Technical Notes.Appendix A: Sampling Distributions, the.Foundation of Statistics.Appendix B: All about Variance.Appendix C: A Primer on Asymptotics.Appendix D: Exercises.Appendix E: Answers to Even-numbered Questions.Glossary.Bibliography.Name Index.Subject Index.
- (source: Nielsen Book Data)9781405182584 20160528
(source: Nielsen Book Data)9781405182584 20160528
Law Library (Crown)
Law Library (Crown) | Status |
---|---|
On reserve: Ask at circulation desk | |
HB139 .K45 2008 | Unknown 2-hour loan |
LAW-243-01
- Course
- LAW-243-01 -- Bayesian Statistics and Econometrics
- Instructor(s)
- Strnad, James Frank
6. Bayesian econometric methods [2007]
- Book
- xxi, 357 p. : ill. ; 26 cm.
- Preface-- 1. The subjective interpretation of probability-- 2. Bayesian inference-- 3. Point estimation-- 4. Frequentist properties of Bayesian estimators-- 5. Interval estimation-- 6. Hypothesis testing-- 7. Prediction-- 8. Choice of prior-- 9. Asymptotic Bayes-- 10. The linear regression model-- 11. Basics of Bayesian computation-- 12. Hierarchical models-- 13. The linear regression model with general covariance matrix-- 14. Latent variable models-- 15. Mixture models-- 16. Bayesian model averaging and selection-- 17. Some stationary time series models-- 18. Some nonstationary time series models-- Appendix-- Index.
- (source: Nielsen Book Data)9780521671736 20160528
(source: Nielsen Book Data)9780521671736 20160528
- Preface-- 1. The subjective interpretation of probability-- 2. Bayesian inference-- 3. Point estimation-- 4. Frequentist properties of Bayesian estimators-- 5. Interval estimation-- 6. Hypothesis testing-- 7. Prediction-- 8. Choice of prior-- 9. Asymptotic Bayes-- 10. The linear regression model-- 11. Basics of Bayesian computation-- 12. Hierarchical models-- 13. The linear regression model with general covariance matrix-- 14. Latent variable models-- 15. Mixture models-- 16. Bayesian model averaging and selection-- 17. Some stationary time series models-- 18. Some nonstationary time series models-- Appendix-- Index.
- (source: Nielsen Book Data)9780521671736 20160528
(source: Nielsen Book Data)9780521671736 20160528
Law Library (Crown)
Law Library (Crown) | Status |
---|---|
On reserve: Ask at circulation desk | |
HB139 .K6359 2007 | Unknown 2-hour loan |
HB139 .K6359 2007 | Unknown 2-hour loan |
LAW-243-01
- Course
- LAW-243-01 -- Bayesian Statistics and Econometrics
- Instructor(s)
- Strnad, James Frank
- Book
- xxii, 625 p. : ill. ; 26 cm.
- 1. Why?-- 2. Concepts and methods from basic probability and statistics-- Part I. A. Single-Level Regression: 3. Linear regression: the basics-- 4. Linear regression: before and after fitting the model-- 5. Logistic regression-- 6. Generalized linear models-- Part I. B. Working with Regression Inferences: 7. Simulation of probability models and statistical inferences-- 8. Simulation for checking statistical procedures and model fits-- 9. Causal inference using regression on the treatment variable-- 10. Causal inference using more advanced models-- Part II. A. Multilevel Regression: 11. Multilevel structures-- 12. Multilevel linear models: the basics-- 13. Multilevel linear models: varying slopes, non-nested models and other complexities-- 14. Multilevel logistic regression-- 15. Multilevel generalized linear models-- Part II. B. Fitting Multilevel Models: 16. Multilevel modeling in bugs and R: the basics-- 17. Fitting multilevel linear and generalized linear models in bugs and R-- 18. Likelihood and Bayesian inference and computation-- 19. Debugging and speeding convergence-- Part III. From Data Collection to Model Understanding to Model Checking: 20. Sample size and power calculations-- 21. Understanding and summarizing the fitted models-- 22. Analysis of variance-- 23. Causal inference using multilevel models-- 24. Model checking and comparison-- 25. Missing data imputation-- Appendixes: A. Six quick tips to improve your regression modeling-- B. Statistical graphics for research and presentation-- C. Software-- References.
- (source: Nielsen Book Data)9780521867061 20160618
(source: Nielsen Book Data)9780521867061 20160618
- 1. Why?-- 2. Concepts and methods from basic probability and statistics-- Part I. A. Single-Level Regression: 3. Linear regression: the basics-- 4. Linear regression: before and after fitting the model-- 5. Logistic regression-- 6. Generalized linear models-- Part I. B. Working with Regression Inferences: 7. Simulation of probability models and statistical inferences-- 8. Simulation for checking statistical procedures and model fits-- 9. Causal inference using regression on the treatment variable-- 10. Causal inference using more advanced models-- Part II. A. Multilevel Regression: 11. Multilevel structures-- 12. Multilevel linear models: the basics-- 13. Multilevel linear models: varying slopes, non-nested models and other complexities-- 14. Multilevel logistic regression-- 15. Multilevel generalized linear models-- Part II. B. Fitting Multilevel Models: 16. Multilevel modeling in bugs and R: the basics-- 17. Fitting multilevel linear and generalized linear models in bugs and R-- 18. Likelihood and Bayesian inference and computation-- 19. Debugging and speeding convergence-- Part III. From Data Collection to Model Understanding to Model Checking: 20. Sample size and power calculations-- 21. Understanding and summarizing the fitted models-- 22. Analysis of variance-- 23. Causal inference using multilevel models-- 24. Model checking and comparison-- 25. Missing data imputation-- Appendixes: A. Six quick tips to improve your regression modeling-- B. Statistical graphics for research and presentation-- C. Software-- References.
- (source: Nielsen Book Data)9780521867061 20160618
(source: Nielsen Book Data)9780521867061 20160618
Law Library (Crown)
Law Library (Crown) | Status |
---|---|
On reserve: Ask at circulation desk | |
HA31.3 .G45 2007 | Unknown 2-hour loan |
LAW-243-01
- Course
- LAW-243-01 -- Bayesian Statistics and Econometrics
- Instructor(s)
- Strnad, James Frank
- Book
- xxix, 528 p. : ill. ; 25 cm.
- List of Figures. List of Tables. List of Games. Preface. Contents and Purpose. Changes in the Second Edition (1994). Changes in the Third Edition (2001). Changes in the Fourth Edition (2006). Using the Book. The Level of Mathematics. Other Books. Contact Information. Acknowledgements. Introduction. History. Game Theory's Method. Exemplifying Theory. This Book's Style. Notes. PART 1: GAME THEORY. 1. The Rules of the Game. Definitions. Dominated and Dominant Strategies: The Prisoner's Dilemma. Iterated Dominance: The Battle of the Bismarck Sea. Nash Equilibrium: Boxed Pigs, The Battle of the Sexes and Ranked Coordination. Focal Points. Notes. Problems. Classroom Game. 2. Information. The Strategic and Extensive Forms of a Game. Information Sets. Perfect, Certain, Symmetric, and Complete Information. The Harsanyi Transformation and Bayesian Games. Example: The Png Settlement Game. Notes. Problems. Classroom Game. 3. Mixed and Continuous Strategies. Mixed Strategies: The Welfare Game. The Payoff-equating Method and Games of Timing. Mixed Strategies with General Parameters and N Players: The Civic Duty Game. Randomizing is not Always Mixing: The Auditing Game. Continuous Strategies: The Cournot Game. Continuous Strategies: The Bertrand Game, Strategic Complements, and Strategic. Substitutes. Existence of Equilibrium. Notes. Problems. Classroom Game. 4. Dynamic Games with Symmetric Information. Subgame Perfectness. An Example of Perfectness: Entry Deterrence I. Credible Threats, Sunk Costs, and the Open-Set Problem in the Game of Nuisance Suits. Recoordination to Pareto-dominant Equilibria in Subgames: Pareto Perfection. Notes. Problems. Classroom Game. 5. Reputation and Repeated Games with Symmetric Information. Finitely Repeated Games and the Chainstore Paradox. Infinitely Repeated Games, Minimax Punishments, and the Folk Theorem. Reputation: The One-sided Prisoner's Dilemma. Product Quality in an Infinitely Repeated Game. Markov Equilibria and Overlapping Generations: Customer Switching Costs. Evolutionary Equilibrium: The Hawk-Dove Game. Notes. Problems. Classroom Game. 6. Dynamic Games with Incomplete Information. Perfect Bayesian Equilibrium: Entry Deterrence II and III. Refining Perfect Bayesian Equilibrium in the Entry Deterrence and PhD Admissions Games. The Importance of Common Knowledge: Entry Deterrence IV and V. Incomplete Information in the Repeated Prisoner's Dilemma: The Gang of Four Model. The Axelrod Tournament. Credit and the Age of the Firm: The Diamond Model. Notes. Problems. Classroom Game. PART 2: ASYMMETRIC INFORMATION. 7. Moral Hazard: Hidden Actions. Categories of Asymmetric Information Models. A Principal-agent Model: The Production Game. The Incentive Compatibility and Participation Constraints. Optimal Contracts: The Broadway Game. Notes. Problems. Classroom Game. 8. Further Topics in Moral Hazard. Efficiency Wages. Tournaments. Institutions and Agency Problems. Renegotiation: The Repossession Game. State-space Diagrams: Insurance Games I and II. Joint Production by Many Agents: The Holmstrom Teams Model. The Multitask Agency Problem. Notes. Problems. Classroom Game. 9. Adverse Selection. Introduction: Production Game VI. Adverse Selection under Certainty: Lemons I and II. Heterogeneous Tastes: Lemons III and IV. Adverse Selection under Uncertainty: Insurance Game III. Market Microstructure. A Variety of Applications. Adverse Selection and Moral Hazard Combined: Production Game VII. Notes. Problems. Classroom Game. 10. Mechanism Design and Postcontractual Hidden Knowledge. Mechanisms, Unravelling, Cross Checking, and the Revelation Principle. Myerson Mechanism Design. An Example of Postcontractual Hidden Knowledge: The Salesman Game. The Groves Mechanism. Price Discrimination. Rate-of-return Regulation and Government Procurement. Notes. Problems. Classroom Game. 11. Signalling. The Informed Player Moves First: Signalling. Variants on the Signalling Model of Education. General Comments on Signalling in Education. The Informed Player Moves Second: Screening. Two Signals: The Game of Underpricing New Stock Issues. Signal Jamming and Limit Pricing. Countersignalling. Notes. Problems. Classroom Game. PART 3: APPLICATIONS. 12. Bargaining. The Basic Bargaining Problem: Splitting a Pie. The Nash Bargaining Solution. Alternating Offers over Finite Time. Alternating Offers over Infinite Time. Incomplete Information. Setting Up a Way to Bargain: The Myerson-Satterthwaite Mechanism. Notes. Problems. Classroom Game. 13. Auctions. Values Private and Common, Continuous and Discrete. Optimal Strategies under Different Rules in Private-value Auctions. Revenue Equivalence, Risk Aversion, and Uncertainty. Reserve Prices and the Marginal Revenue Approach. Common-value Auctions and the Winner's Curse. Asymmetric Equilibria, Affiliation, and Linkage: The Wallet Game. Notes. Problems. Classroom Game. 14. Pricing. Quantities as Strategies: Cournot Equilibrium Revisited. Capacity Constraints: The Edgeworth Paradox. Location Models. Comparative Statics and Supermodular Games. Vertical Differentiation. Durable Monopoly. Notes. Problems. Classroom Game. Mathematical Appendix. Notation. The Greek Alphabet. Glossary. Formulas and Functions. Probability Distributions. Supermodularity. Fixed Point Theorems. Genericity. Discounting. Risk. References and Name Index. Subject Index.
- (source: Nielsen Book Data)9781405136662 20160603
(source: Nielsen Book Data)9781405136662 20160603
- List of Figures. List of Tables. List of Games. Preface. Contents and Purpose. Changes in the Second Edition (1994). Changes in the Third Edition (2001). Changes in the Fourth Edition (2006). Using the Book. The Level of Mathematics. Other Books. Contact Information. Acknowledgements. Introduction. History. Game Theory's Method. Exemplifying Theory. This Book's Style. Notes. PART 1: GAME THEORY. 1. The Rules of the Game. Definitions. Dominated and Dominant Strategies: The Prisoner's Dilemma. Iterated Dominance: The Battle of the Bismarck Sea. Nash Equilibrium: Boxed Pigs, The Battle of the Sexes and Ranked Coordination. Focal Points. Notes. Problems. Classroom Game. 2. Information. The Strategic and Extensive Forms of a Game. Information Sets. Perfect, Certain, Symmetric, and Complete Information. The Harsanyi Transformation and Bayesian Games. Example: The Png Settlement Game. Notes. Problems. Classroom Game. 3. Mixed and Continuous Strategies. Mixed Strategies: The Welfare Game. The Payoff-equating Method and Games of Timing. Mixed Strategies with General Parameters and N Players: The Civic Duty Game. Randomizing is not Always Mixing: The Auditing Game. Continuous Strategies: The Cournot Game. Continuous Strategies: The Bertrand Game, Strategic Complements, and Strategic. Substitutes. Existence of Equilibrium. Notes. Problems. Classroom Game. 4. Dynamic Games with Symmetric Information. Subgame Perfectness. An Example of Perfectness: Entry Deterrence I. Credible Threats, Sunk Costs, and the Open-Set Problem in the Game of Nuisance Suits. Recoordination to Pareto-dominant Equilibria in Subgames: Pareto Perfection. Notes. Problems. Classroom Game. 5. Reputation and Repeated Games with Symmetric Information. Finitely Repeated Games and the Chainstore Paradox. Infinitely Repeated Games, Minimax Punishments, and the Folk Theorem. Reputation: The One-sided Prisoner's Dilemma. Product Quality in an Infinitely Repeated Game. Markov Equilibria and Overlapping Generations: Customer Switching Costs. Evolutionary Equilibrium: The Hawk-Dove Game. Notes. Problems. Classroom Game. 6. Dynamic Games with Incomplete Information. Perfect Bayesian Equilibrium: Entry Deterrence II and III. Refining Perfect Bayesian Equilibrium in the Entry Deterrence and PhD Admissions Games. The Importance of Common Knowledge: Entry Deterrence IV and V. Incomplete Information in the Repeated Prisoner's Dilemma: The Gang of Four Model. The Axelrod Tournament. Credit and the Age of the Firm: The Diamond Model. Notes. Problems. Classroom Game. PART 2: ASYMMETRIC INFORMATION. 7. Moral Hazard: Hidden Actions. Categories of Asymmetric Information Models. A Principal-agent Model: The Production Game. The Incentive Compatibility and Participation Constraints. Optimal Contracts: The Broadway Game. Notes. Problems. Classroom Game. 8. Further Topics in Moral Hazard. Efficiency Wages. Tournaments. Institutions and Agency Problems. Renegotiation: The Repossession Game. State-space Diagrams: Insurance Games I and II. Joint Production by Many Agents: The Holmstrom Teams Model. The Multitask Agency Problem. Notes. Problems. Classroom Game. 9. Adverse Selection. Introduction: Production Game VI. Adverse Selection under Certainty: Lemons I and II. Heterogeneous Tastes: Lemons III and IV. Adverse Selection under Uncertainty: Insurance Game III. Market Microstructure. A Variety of Applications. Adverse Selection and Moral Hazard Combined: Production Game VII. Notes. Problems. Classroom Game. 10. Mechanism Design and Postcontractual Hidden Knowledge. Mechanisms, Unravelling, Cross Checking, and the Revelation Principle. Myerson Mechanism Design. An Example of Postcontractual Hidden Knowledge: The Salesman Game. The Groves Mechanism. Price Discrimination. Rate-of-return Regulation and Government Procurement. Notes. Problems. Classroom Game. 11. Signalling. The Informed Player Moves First: Signalling. Variants on the Signalling Model of Education. General Comments on Signalling in Education. The Informed Player Moves Second: Screening. Two Signals: The Game of Underpricing New Stock Issues. Signal Jamming and Limit Pricing. Countersignalling. Notes. Problems. Classroom Game. PART 3: APPLICATIONS. 12. Bargaining. The Basic Bargaining Problem: Splitting a Pie. The Nash Bargaining Solution. Alternating Offers over Finite Time. Alternating Offers over Infinite Time. Incomplete Information. Setting Up a Way to Bargain: The Myerson-Satterthwaite Mechanism. Notes. Problems. Classroom Game. 13. Auctions. Values Private and Common, Continuous and Discrete. Optimal Strategies under Different Rules in Private-value Auctions. Revenue Equivalence, Risk Aversion, and Uncertainty. Reserve Prices and the Marginal Revenue Approach. Common-value Auctions and the Winner's Curse. Asymmetric Equilibria, Affiliation, and Linkage: The Wallet Game. Notes. Problems. Classroom Game. 14. Pricing. Quantities as Strategies: Cournot Equilibrium Revisited. Capacity Constraints: The Edgeworth Paradox. Location Models. Comparative Statics and Supermodular Games. Vertical Differentiation. Durable Monopoly. Notes. Problems. Classroom Game. Mathematical Appendix. Notation. The Greek Alphabet. Glossary. Formulas and Functions. Probability Distributions. Supermodularity. Fixed Point Theorems. Genericity. Discounting. Risk. References and Name Index. Subject Index.
- (source: Nielsen Book Data)9781405136662 20160603
(source: Nielsen Book Data)9781405136662 20160603
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- LAW-243-01 -- Bayesian Statistics and Econometrics
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- Strnad, James Frank
9. Stata base reference manual [2007]
- Book
- 3 v. : ill. ; 24 cm.
- vol. 1. A-H
- v. 2. I-P
- v. 3. Q-Z.
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- LAW-243-01 -- Bayesian Statistics and Econometrics
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- vi, 548 p. : ill. ; 24 cm.
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- vi, 618 p. : ill. ; 24 cm.
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- Strnad, James Frank
13. Stata user's guide : release 10. [2007]
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- ix, 356 p. : ill. ; 24 cm.
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LAW-243-01
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- LAW-243-01 -- Bayesian Statistics and Econometrics
- Instructor(s)
- Strnad, James Frank
- Book
- xi, 300 p. : ill. ; 25 cm.
- Preface.1. Introduction.1.1 Two Examples.1.1.1 Public School Class Sizes.1.1.2 Value at Risk.1.2 Observables, Unobservables, and Objects of Interest.1.3 Conditioning and Updating.1.4 Simulators.1.5 Modeling.1.6 Decisionmaking.2. Elements of Bayesian Inference.2.1 Basics.2.2 Sufficiency, Ancillarity, and Nuisance Parameters.2.2.1 Sufficiency.2.2.2 Ancillarity.2.2.3 Nuisance Parameters.2.3 Conjugate Prior Distributions.2.4 Bayesian Decision Theory and Point Estimation.2.5 Credible Sets.2.6 Model Comparison.2.6.1 Marginal Likelihoods.2.6.2 Predictive Densities.3. Topics in Bayesian Inference.3.1 Hierarchical Priors and Latent Variables.3.2 Improper Prior Distributions.3.3 Prior Robustness and the Density Ratio Class.3.4 Asymptotic Analysis.3.5 The Likelihood Principle.4. Posterior Simulation.4.1 Direct Sampling, .4.2 Acceptance and Importance Sampling.4.2.1 Acceptance Sampling.4.2.2 Importance Sampling.4.3 Markov Chain Monte Carlo.4.3.1 The Gibbs Sampler.4.3.2 The Metropolis-Hastings Algorithm.4.4 Variance Reduction.4.4.1 Concentrated Expectations.4.4.2 Antithetic Sampling.4.5 Some Continuous State Space Markov Chain Theory.4.5.1 Convergence of the Gibbs Sampler.4.5.2 Convergence of the Metropolis-Hastings Algorithm.4.6 Hybrid Markov Chain Monte Carlo Methods.4.6.1 Transition Mixtures.4.6.2 Metropolis within Gibbs.4.7 Numerical Accuracy and Convergence in Markov Chain Monte Carlo.5. Linear Models.5.1 BACC and the Normal Linear Regression Model.5.2 Seemingly Unrelated Regressions Models.5.3 Linear Constraints in the Linear Model.5.3.1 Linear Inequality Constraints.5.3.2 Conjectured Linear Restrictions, Linear Inequality Constraints, and Covariate Selection.5.4 Nonlinear Regression.5.4.1 Nonlinear Regression with Smoothness Priors.5.4.2 Nonlinear Regression with Basis Functions.6. Modeling with Latent Variables.6.1 Censored Normal Linear Models.6.2 Probit Linear Models.6.3 The Independent Finite State Model.6.4 Modeling with Mixtures of Normal Distributions.6.4.1 The Independent Student-t Linear Model.6.4.2 Normal Mixture Linear Models.6.4.3 Generalizing the Observable Outcomes.7. Modeling for Time Series.7.1 Linear Models with Serial Correlation.7.2 The First-Order Markov Finite State Model.7.2.1 Inference in the Nonstationary Model.7.2.2 Inference in the Stationary Model.7.3 Markov Normal Mixture Linear Model.8. Bayesian Investigation.8.1 Implementing Simulation Methods.8.1.1 Density Ratio Tests.8.1.2 Joint Distribution Tests.8.2 Formal Model Comparison.8.2.1 Bayes Factors for Modeling with Common Likelihoods.8.2.2 Marginal Likelihood Approximation Using Importance Sampling.8.2.3 Marginal Likelihood Approximation Using Gibbs Sampling.8.2.4 Density Ratio Marginal Likelihood Approximation.8.3 Model Specification.8.3.1 Prior Predictive Analysis.8.3.2 Posterior Predictive Analysis.8.4 Bayesian Communication.8.5 Density Ratio Robustness Bounds.Bibliography.Author Index.Subject Index.
- (source: Nielsen Book Data)9780471679325 20160528
(source: Nielsen Book Data)9780471679325 20160528
- Preface.1. Introduction.1.1 Two Examples.1.1.1 Public School Class Sizes.1.1.2 Value at Risk.1.2 Observables, Unobservables, and Objects of Interest.1.3 Conditioning and Updating.1.4 Simulators.1.5 Modeling.1.6 Decisionmaking.2. Elements of Bayesian Inference.2.1 Basics.2.2 Sufficiency, Ancillarity, and Nuisance Parameters.2.2.1 Sufficiency.2.2.2 Ancillarity.2.2.3 Nuisance Parameters.2.3 Conjugate Prior Distributions.2.4 Bayesian Decision Theory and Point Estimation.2.5 Credible Sets.2.6 Model Comparison.2.6.1 Marginal Likelihoods.2.6.2 Predictive Densities.3. Topics in Bayesian Inference.3.1 Hierarchical Priors and Latent Variables.3.2 Improper Prior Distributions.3.3 Prior Robustness and the Density Ratio Class.3.4 Asymptotic Analysis.3.5 The Likelihood Principle.4. Posterior Simulation.4.1 Direct Sampling, .4.2 Acceptance and Importance Sampling.4.2.1 Acceptance Sampling.4.2.2 Importance Sampling.4.3 Markov Chain Monte Carlo.4.3.1 The Gibbs Sampler.4.3.2 The Metropolis-Hastings Algorithm.4.4 Variance Reduction.4.4.1 Concentrated Expectations.4.4.2 Antithetic Sampling.4.5 Some Continuous State Space Markov Chain Theory.4.5.1 Convergence of the Gibbs Sampler.4.5.2 Convergence of the Metropolis-Hastings Algorithm.4.6 Hybrid Markov Chain Monte Carlo Methods.4.6.1 Transition Mixtures.4.6.2 Metropolis within Gibbs.4.7 Numerical Accuracy and Convergence in Markov Chain Monte Carlo.5. Linear Models.5.1 BACC and the Normal Linear Regression Model.5.2 Seemingly Unrelated Regressions Models.5.3 Linear Constraints in the Linear Model.5.3.1 Linear Inequality Constraints.5.3.2 Conjectured Linear Restrictions, Linear Inequality Constraints, and Covariate Selection.5.4 Nonlinear Regression.5.4.1 Nonlinear Regression with Smoothness Priors.5.4.2 Nonlinear Regression with Basis Functions.6. Modeling with Latent Variables.6.1 Censored Normal Linear Models.6.2 Probit Linear Models.6.3 The Independent Finite State Model.6.4 Modeling with Mixtures of Normal Distributions.6.4.1 The Independent Student-t Linear Model.6.4.2 Normal Mixture Linear Models.6.4.3 Generalizing the Observable Outcomes.7. Modeling for Time Series.7.1 Linear Models with Serial Correlation.7.2 The First-Order Markov Finite State Model.7.2.1 Inference in the Nonstationary Model.7.2.2 Inference in the Stationary Model.7.3 Markov Normal Mixture Linear Model.8. Bayesian Investigation.8.1 Implementing Simulation Methods.8.1.1 Density Ratio Tests.8.1.2 Joint Distribution Tests.8.2 Formal Model Comparison.8.2.1 Bayes Factors for Modeling with Common Likelihoods.8.2.2 Marginal Likelihood Approximation Using Importance Sampling.8.2.3 Marginal Likelihood Approximation Using Gibbs Sampling.8.2.4 Density Ratio Marginal Likelihood Approximation.8.3 Model Specification.8.3.1 Prior Predictive Analysis.8.3.2 Posterior Predictive Analysis.8.4 Bayesian Communication.8.5 Density Ratio Robustness Bounds.Bibliography.Author Index.Subject Index.
- (source: Nielsen Book Data)9780471679325 20160528
(source: Nielsen Book Data)9780471679325 20160528
Law Library (Crown)
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On reserve: Ask at circulation desk | |
HB139 .G478 2005 | Unknown 2-hour loan |
HB139 .G478 2005 | Unknown 2-hour loan |
LAW-243-01
- Course
- LAW-243-01 -- Bayesian Statistics and Econometrics
- Instructor(s)
- Strnad, James Frank
- Book
- xix, 407 p. : ill. ; 24 cm.
- Preface.I Casual inference and observational studies.1 An overview of methods for causal inference from observational studies, by Sander Greenland.1.1 Introduction.1.2 Approaches based on causal models.1.3 Canonical inference.1.4 Methodologic modeling.1.5 Conclusion.2 Matching in observational studies, by Paul R. Rosenbaum.2.1 The role of matching in observational studies.2.2 Why match?2.3 Two key issues: balance and structure.2.4 Additional issues.3 Estimating causal effects in nonexperimental studies, by Rajeev Dehejia.3.1 Introduction.3.2 Identifying and estimating the average treatment effect.3.3 The NSWdata.3.4 Propensity score estimates.3.5 Conclusions.4 Medication cost sharing and drug spending in Medicare, by Alyce S. Adams.4.1 Methods.4.2 Results.4.3 Study limitations.4.4 Conclusions and policy implications.5 A comparison of experimental and observational data analyses, by Jennifer L. Hill, Jerome P. Reiter, and Elaine L. Zanutto.5.1 Experimental sample.5.2 Constructed observational study.5.3 Concluding remarks.6 Fixing broken experiments using the propensity score, by Bruce Sacerdote.6.1 Introduction.6.2 The lottery data.6.3 Estimating the propensity scores.6.4 Results.6.5 Concluding remarks.7 The propensity score with continuous treatments, by Keisuke Hirano and Guido W. Imbens.7.1 Introduction.7.2 The basic framework.7.3 Bias removal using the GPS.7.4 Estimation and inference.7.5 Application: the Imbens-Rubin-Sacerdote lottery sample.7.6 Conclusion.8 Causal inference with instrumental variables, by Junni L. Zhang.8.1 Introduction.8.2 Key assumptions for the LATE interpretation of the IV estimand.8.3 Estimating causal effects with IV.8.4 Some recent applications.8.5 Discussion.9 Principal stratification, by Constantine E. Frangakis.9.1 Introduction: partially controlled studies.9.2 Examples of partially controlled studies.9.3 Principal stratification.9.4 Estimands.9.5 Assumptions.9.6 Designs and polydesigns.II Missing data modeling.10 Nonresponse adjustment in government statistical agencies: constraints, inferential goals, and robustness issues, by John L. Eltinge.10.1 Introduction: a wide spectrum of nonresponse adjustment efforts in government statistical agencies.10.2 Constraints.10.3 Complex estimand structures, inferential goals, and utility functions.10.4 Robustness.10.5 Closing remarks.11 Bridging across changes in classification systems, by Nathaniel Schenker.11.1 Introduction.11.2 Multiple imputation to achieve comparability of industry and occupation codes.11.3 Bridging the transition from single-race reporting to multiple-race reporting.11.4 Conclusion.12 Representing the Census undercount by multiple imputation of households, by Alan M. Zaslavsky.12.1 Introduction.12.2 Models.12.3 Inference.12.4 Simulation evaluations.12.5 Conclusion.13 Statistical disclosure techniques based on multiple imputation, by Roderick J. A. Little, Fang Liu, and Trivellore E. Raghunathan.13.1 Introduction.13.2 Full synthesis.13.3 SMIKe andMIKe.13.4 Analysis of synthetic samples.13.5 An application.13.6 Conclusions.14 Designs producing balanced missing data: examples from the National Assessment of Educational Progress, by Neal Thomas.14.1 Introduction.14.2 Statistical methods in NAEP.14.3 Split and balanced designs for estimating population parameters.14.4 Maximum likelihood estimation.14.5 The role of secondary covariates.14.6 Conclusions.15 Propensity score estimation with missing data, by Ralph B. D'Agostino Jr.15.1 Introduction.15.2 Notation.15.3 Applied example:March of Dimes data.15.4 Conclusion and future directions.16 Sensitivity to nonignorability in frequentist inference, by Guoguang Ma and Daniel F. Heitjan.16.1 Missing data in clinical trials.16.2 Ignorability and bias.16.3 A nonignorable selection model.16.4 Sensitivity of the mean and variance.16.5 Sensitivity of the power.16.6 Sensitivity of the coverage probability.16.7 An example.16.8 Discussion.III Statistical modeling and computation.17 Statistical modeling and computation, by D. Michael Titterington.17.1 Regression models.17.2 Latent-variable problems.17.3 Computation: non-Bayesian.17.4 Computation: Bayesian.17.5 Prospects for the future.18 Treatment effects in before-after data, by Andrew Gelman.18.1 Default statistical models of treatment effects.18.2 Before-after correlation is typically larger for controls than for treated units.18.3 A class of models for varying treatment effects.18.4 Discussion.19 Multimodality in mixture models and factor models, by Eric Loken.19.1 Multimodality in mixture models.19.2 Multimodal posterior distributions in continuous latent variable models.19.3 Summary.20 Modeling the covariance and correlation matrix of repeated measures, by W. John Boscardin and Xiao Zhang.20.1 Introduction.20.2 Modeling the covariance matrix.20.3 Modeling the correlation matrix.20.4 Modeling a mixed covariance-correlation matrix.20.5 Nonzero means and unbalanced data.20.6 Multivariate probit model.20.7 Example: covariance modeling.20.8 Example: mixed data.21 Robit regression: a simple robust alternative to logistic and probit regression, by Chuanhai Liu.21.1 Introduction.21.2 The robit model.21.3 Robustness of likelihood-based inference using logistic, probit, and robit regression models.21.4 Complete data for simple maximum likelihood estimation.21.5 Maximum likelihood estimation using EM-type algorithms.21.6 A numerical example.21.7 Conclusion.22 Using EM and data augmentation for the competing risks model, by Radu V. Craiu and Thierry Duchesne.22.1 Introduction.22.2 The model.22.3 EM-based analysis.22.4 Bayesian analysis.22.5 Example.22.6 Discussion and further work.23 Mixed effects models and the EM algorithm, by Florin Vaida, Xiao-Li Meng, and Ronghui Xu.23.1 Introduction.23.2 Binary regression with random effects.23.3 Proportional hazards mixed-effects models.24 The sampling/importance resampling algorithm, by Kim-Hung Li.24.1 Introduction.24.2 SIR algorithm.24.3 Selection of the pool size.24.4 Selection criterion of the importance sampling distribution.24.5 The resampling algorithms.24.6 Discussion.IV Applied Bayesian inference.25 Whither applied Bayesian inference?, by Bradley P. Carlin.25.1 Where we've been.25.2 Where we are.25.3 Where we're going.26 Efficient EM-type algorithms for fitting spectral lines in high-energy astrophysics, by David A. van Dyk and Taeyoung Park.26.1 Application-specific statistical methods .26.2 The Chandra X-ray observatory.26.3 Fitting narrow emission lines.26.4 Model checking and model selection.27 Improved predictions of lynx trappings using a biological model, by Cavan Reilly and Angelique Zeringue.27.1 Introduction.27.2 The current best model.27.3 Biological models for predator prey systems.27.4 Some statistical models based on the Lotka-Volterra system.27.5 Computational aspects of posterior inference.27.6 Posterior predictive checks and model expansion.27.7 Prediction with the posterior mode.27.8 Discussion.28 Record linkage using finite mixture models, by Michael D. Larsen.28.1 Introduction to record linkage.28.2 Record linkage.28.3 Mixture models.28.4 Application.28.5 Analysis of linked files.28.6 Bayesian hierarchical record linkage.28.7 Summary.29 Identifying likely duplicates by record linkage in a survey of prostitutes, by Thomas R. Belin, Hemant Ishwaran, Naihua Duan, Sandra H. Berry, and David E. Kanouse.29.1 Concern about duplicates in an anonymous survey.29.2 General frameworks for record linkage.29.3 Estimating probabilities of duplication in the Los Angeles Women's Health Risk Study.29.4 Discussion.30 Applying structural equation models with incomplete data, by Hal S. Stern and Yoonsook Jeon.30.1 Structural equation models.30.2 Bayesian inference for structural equation models.30.3 Iowa Youth and Families Project example.30.4 Summary and discussion.31 Perceptual scaling, by Ying Nian Wu, Cheng-En Guo, and Song Chun Zhu.31.1 Introduction.31.2 Sparsity and minimax entropy.31.3 Complexity scaling law.31.4 Perceptibility scaling law.31.5 Texture = imperceptible structures.31.6 Perceptibility and sparsity.References.Index.
- (source: Nielsen Book Data)9780470090435 20160528
(source: Nielsen Book Data)9780470090435 20160528
- Preface.I Casual inference and observational studies.1 An overview of methods for causal inference from observational studies, by Sander Greenland.1.1 Introduction.1.2 Approaches based on causal models.1.3 Canonical inference.1.4 Methodologic modeling.1.5 Conclusion.2 Matching in observational studies, by Paul R. Rosenbaum.2.1 The role of matching in observational studies.2.2 Why match?2.3 Two key issues: balance and structure.2.4 Additional issues.3 Estimating causal effects in nonexperimental studies, by Rajeev Dehejia.3.1 Introduction.3.2 Identifying and estimating the average treatment effect.3.3 The NSWdata.3.4 Propensity score estimates.3.5 Conclusions.4 Medication cost sharing and drug spending in Medicare, by Alyce S. Adams.4.1 Methods.4.2 Results.4.3 Study limitations.4.4 Conclusions and policy implications.5 A comparison of experimental and observational data analyses, by Jennifer L. Hill, Jerome P. Reiter, and Elaine L. Zanutto.5.1 Experimental sample.5.2 Constructed observational study.5.3 Concluding remarks.6 Fixing broken experiments using the propensity score, by Bruce Sacerdote.6.1 Introduction.6.2 The lottery data.6.3 Estimating the propensity scores.6.4 Results.6.5 Concluding remarks.7 The propensity score with continuous treatments, by Keisuke Hirano and Guido W. Imbens.7.1 Introduction.7.2 The basic framework.7.3 Bias removal using the GPS.7.4 Estimation and inference.7.5 Application: the Imbens-Rubin-Sacerdote lottery sample.7.6 Conclusion.8 Causal inference with instrumental variables, by Junni L. Zhang.8.1 Introduction.8.2 Key assumptions for the LATE interpretation of the IV estimand.8.3 Estimating causal effects with IV.8.4 Some recent applications.8.5 Discussion.9 Principal stratification, by Constantine E. Frangakis.9.1 Introduction: partially controlled studies.9.2 Examples of partially controlled studies.9.3 Principal stratification.9.4 Estimands.9.5 Assumptions.9.6 Designs and polydesigns.II Missing data modeling.10 Nonresponse adjustment in government statistical agencies: constraints, inferential goals, and robustness issues, by John L. Eltinge.10.1 Introduction: a wide spectrum of nonresponse adjustment efforts in government statistical agencies.10.2 Constraints.10.3 Complex estimand structures, inferential goals, and utility functions.10.4 Robustness.10.5 Closing remarks.11 Bridging across changes in classification systems, by Nathaniel Schenker.11.1 Introduction.11.2 Multiple imputation to achieve comparability of industry and occupation codes.11.3 Bridging the transition from single-race reporting to multiple-race reporting.11.4 Conclusion.12 Representing the Census undercount by multiple imputation of households, by Alan M. Zaslavsky.12.1 Introduction.12.2 Models.12.3 Inference.12.4 Simulation evaluations.12.5 Conclusion.13 Statistical disclosure techniques based on multiple imputation, by Roderick J. A. Little, Fang Liu, and Trivellore E. Raghunathan.13.1 Introduction.13.2 Full synthesis.13.3 SMIKe andMIKe.13.4 Analysis of synthetic samples.13.5 An application.13.6 Conclusions.14 Designs producing balanced missing data: examples from the National Assessment of Educational Progress, by Neal Thomas.14.1 Introduction.14.2 Statistical methods in NAEP.14.3 Split and balanced designs for estimating population parameters.14.4 Maximum likelihood estimation.14.5 The role of secondary covariates.14.6 Conclusions.15 Propensity score estimation with missing data, by Ralph B. D'Agostino Jr.15.1 Introduction.15.2 Notation.15.3 Applied example:March of Dimes data.15.4 Conclusion and future directions.16 Sensitivity to nonignorability in frequentist inference, by Guoguang Ma and Daniel F. Heitjan.16.1 Missing data in clinical trials.16.2 Ignorability and bias.16.3 A nonignorable selection model.16.4 Sensitivity of the mean and variance.16.5 Sensitivity of the power.16.6 Sensitivity of the coverage probability.16.7 An example.16.8 Discussion.III Statistical modeling and computation.17 Statistical modeling and computation, by D. Michael Titterington.17.1 Regression models.17.2 Latent-variable problems.17.3 Computation: non-Bayesian.17.4 Computation: Bayesian.17.5 Prospects for the future.18 Treatment effects in before-after data, by Andrew Gelman.18.1 Default statistical models of treatment effects.18.2 Before-after correlation is typically larger for controls than for treated units.18.3 A class of models for varying treatment effects.18.4 Discussion.19 Multimodality in mixture models and factor models, by Eric Loken.19.1 Multimodality in mixture models.19.2 Multimodal posterior distributions in continuous latent variable models.19.3 Summary.20 Modeling the covariance and correlation matrix of repeated measures, by W. John Boscardin and Xiao Zhang.20.1 Introduction.20.2 Modeling the covariance matrix.20.3 Modeling the correlation matrix.20.4 Modeling a mixed covariance-correlation matrix.20.5 Nonzero means and unbalanced data.20.6 Multivariate probit model.20.7 Example: covariance modeling.20.8 Example: mixed data.21 Robit regression: a simple robust alternative to logistic and probit regression, by Chuanhai Liu.21.1 Introduction.21.2 The robit model.21.3 Robustness of likelihood-based inference using logistic, probit, and robit regression models.21.4 Complete data for simple maximum likelihood estimation.21.5 Maximum likelihood estimation using EM-type algorithms.21.6 A numerical example.21.7 Conclusion.22 Using EM and data augmentation for the competing risks model, by Radu V. Craiu and Thierry Duchesne.22.1 Introduction.22.2 The model.22.3 EM-based analysis.22.4 Bayesian analysis.22.5 Example.22.6 Discussion and further work.23 Mixed effects models and the EM algorithm, by Florin Vaida, Xiao-Li Meng, and Ronghui Xu.23.1 Introduction.23.2 Binary regression with random effects.23.3 Proportional hazards mixed-effects models.24 The sampling/importance resampling algorithm, by Kim-Hung Li.24.1 Introduction.24.2 SIR algorithm.24.3 Selection of the pool size.24.4 Selection criterion of the importance sampling distribution.24.5 The resampling algorithms.24.6 Discussion.IV Applied Bayesian inference.25 Whither applied Bayesian inference?, by Bradley P. Carlin.25.1 Where we've been.25.2 Where we are.25.3 Where we're going.26 Efficient EM-type algorithms for fitting spectral lines in high-energy astrophysics, by David A. van Dyk and Taeyoung Park.26.1 Application-specific statistical methods .26.2 The Chandra X-ray observatory.26.3 Fitting narrow emission lines.26.4 Model checking and model selection.27 Improved predictions of lynx trappings using a biological model, by Cavan Reilly and Angelique Zeringue.27.1 Introduction.27.2 The current best model.27.3 Biological models for predator prey systems.27.4 Some statistical models based on the Lotka-Volterra system.27.5 Computational aspects of posterior inference.27.6 Posterior predictive checks and model expansion.27.7 Prediction with the posterior mode.27.8 Discussion.28 Record linkage using finite mixture models, by Michael D. Larsen.28.1 Introduction to record linkage.28.2 Record linkage.28.3 Mixture models.28.4 Application.28.5 Analysis of linked files.28.6 Bayesian hierarchical record linkage.28.7 Summary.29 Identifying likely duplicates by record linkage in a survey of prostitutes, by Thomas R. Belin, Hemant Ishwaran, Naihua Duan, Sandra H. Berry, and David E. Kanouse.29.1 Concern about duplicates in an anonymous survey.29.2 General frameworks for record linkage.29.3 Estimating probabilities of duplication in the Los Angeles Women's Health Risk Study.29.4 Discussion.30 Applying structural equation models with incomplete data, by Hal S. Stern and Yoonsook Jeon.30.1 Structural equation models.30.2 Bayesian inference for structural equation models.30.3 Iowa Youth and Families Project example.30.4 Summary and discussion.31 Perceptual scaling, by Ying Nian Wu, Cheng-En Guo, and Song Chun Zhu.31.1 Introduction.31.2 Sparsity and minimax entropy.31.3 Complexity scaling law.31.4 Perceptibility scaling law.31.5 Texture = imperceptible structures.31.6 Perceptibility and sparsity.References.Index.
- (source: Nielsen Book Data)9780470090435 20160528
(source: Nielsen Book Data)9780470090435 20160528
Law Library (Crown)
Law Library (Crown) | Status |
---|---|
On reserve: Ask at circulation desk | |
QA279.5 .A57 2004 | Unknown 2-hour loan |
LAW-243-01
- Course
- LAW-243-01 -- Bayesian Statistics and Econometrics
- Instructor(s)
- Strnad, James Frank
16. Bayesian data analysis [2004]
- Book
- xxv, 668 p. : ill., maps ; 25 cm.
- FUNDAMENTALS OF BAYESIAN INFERENCE Background Single-Parameter Models Introduction to Multiparameter Models Large-Sample Inference and Connections to Standard Statistical Methods FUNDAMENTALS OF BAYESIAN DATA ANALYSIS Hierarchical Models Model Checking and Improvement Modeling Accounting for Data Collection Connections and Controversies General Advice ADVANCED COMPUTATION Overview of Computation Posterior Simulation Approximations Based on Posterior Modes Topics in Computation REGRESSION MODELS Introduction to Regression Models Hierarchical Linear Models Generalized Linear Models Models for Robust Inference and Sensitivity Analysis Analysis of Variance SPECIFIC MODELS AND PROBLEMS Mixture Models Multivariate Models Nonlinear Models Models for Missing Data Decision Analysis APPENDICES A: Standard Probability Distributions B: Outline of Proofs of Asymptotic Theorems C: Example of Computation in R and Bugs References.
- (source: Nielsen Book Data)9781584883883 20160605
(source: Nielsen Book Data)9781584883883 20160605
- FUNDAMENTALS OF BAYESIAN INFERENCE Background Single-Parameter Models Introduction to Multiparameter Models Large-Sample Inference and Connections to Standard Statistical Methods FUNDAMENTALS OF BAYESIAN DATA ANALYSIS Hierarchical Models Model Checking and Improvement Modeling Accounting for Data Collection Connections and Controversies General Advice ADVANCED COMPUTATION Overview of Computation Posterior Simulation Approximations Based on Posterior Modes Topics in Computation REGRESSION MODELS Introduction to Regression Models Hierarchical Linear Models Generalized Linear Models Models for Robust Inference and Sensitivity Analysis Analysis of Variance SPECIFIC MODELS AND PROBLEMS Mixture Models Multivariate Models Nonlinear Models Models for Missing Data Decision Analysis APPENDICES A: Standard Probability Distributions B: Outline of Proofs of Asymptotic Theorems C: Example of Computation in R and Bugs References.
- (source: Nielsen Book Data)9781584883883 20160605
(source: Nielsen Book Data)9781584883883 20160605
Law Library (Crown)
Law Library (Crown) | Status |
---|---|
On reserve: Ask at circulation desk | |
QA279.5 .B386 2004 | Unknown 2-hour loan |
QA279.5 .B386 2004 | Unknown 2-hour loan |
LAW-243-01
- Course
- LAW-243-01 -- Bayesian Statistics and Econometrics
- Instructor(s)
- Strnad, James Frank
17. Bayesian theory [1994]
- Book
- xiv, 586 p. : ill. ; 23 cm.
- Foundations-- Generalisations-- Modelling-- Inference-- Remodelling-- Appendices-- References-- Indexes.
- (source: Nielsen Book Data)9780471494645 20160528
(source: Nielsen Book Data)9780471494645 20160528
A controversial philosophical approach to statistics following the work of Rev Thomas Bayes (1701). To solve a problem or to make a decision, the Bayesian collects data from all possible theories and assigns a probability to them. This generates a prior distribution from which, workable parameters are determined and complex calculations are made.
(source: Nielsen Book Data)9780471924166 20160528
- Foundations-- Generalisations-- Modelling-- Inference-- Remodelling-- Appendices-- References-- Indexes.
- (source: Nielsen Book Data)9780471494645 20160528
(source: Nielsen Book Data)9780471494645 20160528
A controversial philosophical approach to statistics following the work of Rev Thomas Bayes (1701). To solve a problem or to make a decision, the Bayesian collects data from all possible theories and assigns a probability to them. This generates a prior distribution from which, workable parameters are determined and complex calculations are made.
(source: Nielsen Book Data)9780471924166 20160528
Law Library (Crown)
Law Library (Crown) | Status |
---|---|
On reserve: Ask at circulation desk | |
QA279.5 .B47 2004 | Unknown 2-hour loan |
LAW-243-01
- Course
- LAW-243-01 -- Bayesian Statistics and Econometrics
- Instructor(s)
- Strnad, James Frank
- Book
- xxii, 382 p. : ill. ; 24 cm.
- A Framework for Item Response Models.- Descriptive and Explanatory Item Response Models.- Models for Polytomous Data.- An Introduction to (Generalized) (Non) Linear Mixed Models.- Person Regression Models.- Models with Item and Item Group Predictors.- Person-by-item Predictors.- Multiple Person Dimensions and Latent Item Predictors.- Latent Item Predictors with Fixed Effects.- Models for Residual Dependencies.- Mixture Models.- Estimation and Software.
- (source: Nielsen Book Data)9780387402758 20160528
(source: Nielsen Book Data)9780387402758 20160528
- A Framework for Item Response Models.- Descriptive and Explanatory Item Response Models.- Models for Polytomous Data.- An Introduction to (Generalized) (Non) Linear Mixed Models.- Person Regression Models.- Models with Item and Item Group Predictors.- Person-by-item Predictors.- Multiple Person Dimensions and Latent Item Predictors.- Latent Item Predictors with Fixed Effects.- Models for Residual Dependencies.- Mixture Models.- Estimation and Software.
- (source: Nielsen Book Data)9780387402758 20160528
(source: Nielsen Book Data)9780387402758 20160528
Law Library (Crown)
Law Library (Crown) | Status |
---|---|
On reserve: Ask at circulation desk | |
HA32 .E96 2004 | Unknown 2-hour loan |
LAW-243-01
- Course
- LAW-243-01 -- Bayesian Statistics and Econometrics
- Instructor(s)
- Strnad, James Frank
- Book
- xiv, 401 p. : ill. ; 26 cm.
- Introduction. 1. The Bayesian Algorithm. 2. Prediction and Model Checking. 3. Linear Regression. 4. Bayesian Calculations. 5. Nonlinear Regression Models. 6. Randomized, Controlled and Observational Data. 7. Models for Panel Data. 8. Instrumental Variables. 9. Some Time Series Models. Appendix 1: A Conversion Manual. Appendix 2: Programming. Appendix 3: BUGS. Index.
- (source: Nielsen Book Data)9781405117203 20160610
(source: Nielsen Book Data)9781405117197 20160610
In this new and expanding area, Tony Lancaster's text is the first comprehensive introduction to the Bayesian way of doing applied economics. * Uses clear explanations and practical illustrations and problems to present innovative, computer--intensive ways for applied economists to use the Bayesian method; * Emphasizes computation and the study of probability distributions by computer sampling; * Covers all the standard econometric models, including linear and non--linear regression using cross--sectional, time series, and panel data; * Details causal inference and inference about structural econometric models; * Includes numerical and graphical examples in each chapter, demonstrating their solutions using the S programming language and Bugs software * Supported by online supplements, including Data Sets and Solutions to Problems, at www.blackwellpublishing.com/lancaster.
(source: Nielsen Book Data)9781405117203 20160610
- Introduction. 1. The Bayesian Algorithm. 2. Prediction and Model Checking. 3. Linear Regression. 4. Bayesian Calculations. 5. Nonlinear Regression Models. 6. Randomized, Controlled and Observational Data. 7. Models for Panel Data. 8. Instrumental Variables. 9. Some Time Series Models. Appendix 1: A Conversion Manual. Appendix 2: Programming. Appendix 3: BUGS. Index.
- (source: Nielsen Book Data)9781405117203 20160610
(source: Nielsen Book Data)9781405117197 20160610
In this new and expanding area, Tony Lancaster's text is the first comprehensive introduction to the Bayesian way of doing applied economics. * Uses clear explanations and practical illustrations and problems to present innovative, computer--intensive ways for applied economists to use the Bayesian method; * Emphasizes computation and the study of probability distributions by computer sampling; * Covers all the standard econometric models, including linear and non--linear regression using cross--sectional, time series, and panel data; * Details causal inference and inference about structural econometric models; * Includes numerical and graphical examples in each chapter, demonstrating their solutions using the S programming language and Bugs software * Supported by online supplements, including Data Sets and Solutions to Problems, at www.blackwellpublishing.com/lancaster.
(source: Nielsen Book Data)9781405117203 20160610
Law Library (Crown)
Law Library (Crown) | Status |
---|---|
On reserve: Ask at circulation desk | |
HB139 .L353 2004 | Unknown 2-hour loan |
HB139 .L353 2004 | Unknown 2-hour loan |
LAW-243-01
- Course
- LAW-243-01 -- Bayesian Statistics and Econometrics
- Instructor(s)
- Strnad, James Frank
20. Bayesian econometrics [2003]
- Book
- xiv, 359 p. : ill. ; 25 cm.
- Preface.1. An Overview of Bayesian Econometrics.2. The Normal Linear Regression Model with Natural Conjugate Prior and a Single Explanatory Variable.3. The Normal Linear Regression Model with Natural Conjugate Prior and Many Explanatory Variables.4. The Normal Linear Regression Model with Other Priors.5. The Nonlinear Regression Model.6. The Linear Regression Model with General Error Covariance Matrix.7. The Linear Regression Model with Panel Data.8. Introduction to Time Series: State Space Models.9. Qualitative and Limited Dependent Variable Models.10. Flexible Models: Nonparametric and Semi-Parametric Methods.11. Bayesian Model Averaging.12. Other Models, Methods and Issues.Appendix A: Introduction to Matrix Algebra.Appendix B: Introduction to Probability and Statistics.Bibliography.Index.
- (source: Nielsen Book Data)9780470845677 20160528
(source: Nielsen Book Data)9780470845677 20160528
- Preface.1. An Overview of Bayesian Econometrics.2. The Normal Linear Regression Model with Natural Conjugate Prior and a Single Explanatory Variable.3. The Normal Linear Regression Model with Natural Conjugate Prior and Many Explanatory Variables.4. The Normal Linear Regression Model with Other Priors.5. The Nonlinear Regression Model.6. The Linear Regression Model with General Error Covariance Matrix.7. The Linear Regression Model with Panel Data.8. Introduction to Time Series: State Space Models.9. Qualitative and Limited Dependent Variable Models.10. Flexible Models: Nonparametric and Semi-Parametric Methods.11. Bayesian Model Averaging.12. Other Models, Methods and Issues.Appendix A: Introduction to Matrix Algebra.Appendix B: Introduction to Probability and Statistics.Bibliography.Index.
- (source: Nielsen Book Data)9780470845677 20160528
(source: Nielsen Book Data)9780470845677 20160528
Law Library (Crown)
Law Library (Crown) | Status |
---|---|
On reserve: Ask at circulation desk | |
HB141 .K6443 2003 | Unknown 2-hour loan |
HB141 .K6443 2003 | Unknown 2-hour loan |
HB141 .K6443 2003 | Unknown 2-hour loan |
LAW-243-01
- Course
- LAW-243-01 -- Bayesian Statistics and Econometrics
- Instructor(s)
- Strnad, James Frank