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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.
Silver built an innovative system for predicting baseball performance, predicted the 2008 election within a hair's breadth, and became a national sensation as a blogger. Drawing on his own groundbreaking work, Silver examines the world of prediction.
Law Library (Crown)
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.
For courses in introductory econometrics. An approach to modern econometrics theory and practice through engaging applications. Ensure students grasp the relevance of econometrics with Introduction to Econometrics--the text that connects modern theory and practice with engaging applications. The third edition builds on the philosophy that applications should drive the theory, not the other way around, while maintaining a focus on currency.
(source: Nielsen Book Data)9781408264331 20160605
Law Library (Crown)
LAW-243-01, LAW-7512-01

3. Basic econometrics [2009]

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
Law Library (Crown)
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
This is the perfect (and essential) supplement for all econometrics classes - from a rigorous first undergraduate course, to a first master's, to a PhD course. This book explains what is going on in textbooks full of proofs and formulas. It offers intuition, skepticism, insights, humor, and practical advice (dos and don'ts). It contains new chapters that cover instrumental variables and computational considerations. It includes additional information on GMM, nonparametrics, and an introduction to wavelets.
(source: Nielsen Book Data)9781405182584 20160528
Law Library (Crown)
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
Written in a crisp and approachable style, Games and Information uses simple modeling techniques and straightforward explanations to provide students with an understanding of game theory and information economics. * Written for introductory courses seeking a little rigor. * The 4th edition brings the material fully up-to-date and includes new end-of-chapter problems and classroom projects, as well as a math appendix. * Accompanied by a comprehensive website featuring solutions to problems and teaching notes.
(source: Nielsen Book Data)9781405136662 20160603
Law Library (Crown)
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
This book brings together a collection of articles on statistical methods relating to missing data analysis, including multiple imputation, propensity scores, instrumental variables, and Bayesian inference. Covering new research topics and real-world examples which do not feature in many standard texts. The book is dedicated to Professor Don Rubin (Harvard). Don Rubin has made fundamental contributions to the study of missing data. Key features of the book include: Comprehensive coverage of an imporant area for both research and applications. Adopts a pragmatic approach to describing a wide range of intermediate and advanced statistical techniques. Covers key topics such as multiple imputation, propensity scores, instrumental variables and Bayesian inference. Includes a number of applications from the social and health sciences. Edited and authored by highly respected researchers in the area.
(source: Nielsen Book Data)9780470090435 20160528
Law Library (Crown)
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
Incorporating new and updated information, this second edition of THE bestselling text in Bayesian data analysis continues to emphasize practice over theory, describing how to conceptualize, perform, and critique statistical analyses from a Bayesian perspective. Its world-class authors provide guidance on all aspects of Bayesian data analysis and include examples of real statistical analyses, based on their own research, that demonstrate how to solve complicated problems. Changes in the new edition include: Stronger focus on MCMC Revision of the computational advice in Part III New chapters on nonlinear models and decision analysis Several additional applied examples from the authors' recent research Additional chapters on current models for Bayesian data analysis such as nonlinear models, generalized linear mixed models, and more Reorganization of chapters 6 and 7 on model checking and data collection Bayesian computation is currently at a stage where there are many reasonable ways to compute any given posterior distribution. However, the best approach is not always clear ahead of time. Reflecting this, the new edition offers a more pluralistic presentation, giving advice on performing computations from many perspectives while making clear the importance of being aware that there are different ways to implement any given iterative simulation computation. The new approach, additional examples, and updated information make Bayesian Data Analysis an excellent introductory text and a reference that working scientists will use throughout their professional life.
(source: Nielsen Book Data)9781584883883 20160605
Law Library (Crown)

8. Bayesian theory [1994]

xiv, 586 p. : ill. ; 23 cm.
  • Foundations-- Generalisations-- Modelling-- Inference-- Remodelling-- Appendices-- References-- Indexes.
  • (source: Nielsen Book Data)9780471494645 20160528
This text, now available in paperback, provides a thorough account of key basic concepts and theoretical results, with particular emphasis on viewing statistical inference as a special case of decision theory. Information-theoretic concepts play a central role in the development, which provides, in particular, a detailed treatment of the problem of specification of so-called "prior ignorance". Written from the authors' committed Bayesian perspective, this work includes an overview of non-Bayesian theories, and each chapter contains a critical re-examination of controversial issues. The level of mathematics used is such that most material is accessible to readers with knowledge of advanced calculus. In particular, no knowledge of abstract measure theory is assumed, and the emphasis throughout is on statistical concepts rather than rigorous mathematics. This text is a source of information for all students and researchers in statistics, mathematics, decision analysis, economic and business studies, and all branches of science and engineering, who wish to further their understanding of Bayesian statistics.
(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)
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
Researchers in many fields are increasingly finding the Bayesian approach to statistics to be an attractive one. This book introduces the reader to the use of Bayesian methods in the field of econometrics at the advanced undergraduate or graduate level. The book is self-contained and does not require that readers have previous training in econometrics. The focus is on models used by applied economists and the computational techniques necessary to implement Bayesian methods when doing empirical work. Topics covered in the book include the regression model (and variants applicable for use with panel data), time series models, models for qualitative or censored data, nonparametric methods and Bayesian model averaging. The book includes numerous empirical examples and the website associated with it contains data sets and computer programs to help the student develop the computational skills of modern Bayesian econometrics.
(source: Nielsen Book Data)9780470845677 20160528
Law Library (Crown)
xvi, 533 p. : col. ill. ; 25 cm.
  • Overview of Supervised Learning.- Linear Methods for Regression.- Linear Methods for Classification.- Basic Expansions and Regularization.- Kernel Methods.- Model Assessment and Selection.- Model Inference and Averaging.- Additive Models, Trees, and Related Methods.- Boosting and Additive Trees.- Neural Networks.- Support Vector Machines and Flexible Discriminates.- Prototype Methods and Nearest Neighbors.- Unsupervised Learning.
  • (source: Nielsen Book Data)9780387952840 20160527
During the past decade there has been an explosion in computation and information technology. With it have come vast amounts of data in a variety of fields such as medicine, biology, finance, and marketing. The challenge of understanding these data has led to the development of new tools in the field of statistics, and spawned new areas such as data mining, machine learning, and bioinformatics. Many of these tools have common underpinnings but are often expressed with different terminology. This book describes the important ideas in these areas in a common conceptual framework. While the approach is statistical, the emphasis is on concepts rather than mathematics. Many examples are given, with a liberal use of color graphics. It should be a valuable resource for statisticians and anyone interested in data mining in science or industry.The book's coverage is broad, from supervised learning (prediction) to unsupervised learning. The many topics include neural networks, support vector machines, classification trees and boosting - the first comprehensive treatment of this topic in any book. This major new edition features many topics not covered in the original, including graphical models, random forests, ensemble methods, least angle regression and path algorithms for the lasso, non-negative matrix factorization, and spectral clustering.There is also a chapter on methods for 'wide' data (p bigger than n), including multiple testing and false discovery rates. Trevor Hastie, Robert Tibshirani, and Jerome Friedman are professors of statistics at Stanford University. They are prominent researchers in this area: Hastie and Tibshirani developed generalized additive models and wrote a popular book of that title. Hastie co-developed much of the statistical modeling software and environment in R/S-PLUS and invented principal curves and surfaces. Tibshirani proposed the lasso and is co-author of the very successful "An Introduction to the Bootstrap". Friedman is the co-inventor of many data-mining tools including CART, MARS, projection pursuit and gradient boosting.
(source: Nielsen Book Data)9780387952840 20160527
Law Library (Crown)
xviii, 588 p. : ill. ; 23 cm.
  • Nature of Bayesian Inference-- Standard Normal Theory Inference Problems-- Bayesian Assessment of Assumptions: Effect of Non-Normality on Inferences About a Population Mean with Generalizations-- Bayesian Assessment of Assumptions: Comparison of Variances-- Random Effect Models-- Analysis of Cross Classification Designs-- Inference About Means with Information from More than One Source: One-Way Classification and Block Designs-- Some Aspects of Multivariate Analysis-- Estimation of Common Regression Coefficients-- Transformation of Data-- Tables-- References-- Indexes.
  • (source: Nielsen Book Data)9780471574286 20160528
The Wiley Classics Library consists of selected books that have become recognized classics in their respective fields. With these new unabridged and inexpensive editions, Wiley hopes to extend the life of these important works by making them available to future generations of mathematicians and scientists. Currently available in the Series: T. W. Anderson The Statistical Analysis of Time Series T. S. Arthanari & Yadolah Dodge Mathematical Programming in Statistics Emil Artin Geometric Algebra Norman T. J. Bailey The Elements of Stochastic Processes with Applications to the Natural Sciences Robert G. Bartle The Elements of Integration and Lebesgue Measure George E. P. Box & George C. Tiao Bayesian Inference in Statistical Analysis R. W. Carter Finite Groups of Lie Type: Conjugacy Classes and Complex Characters R. W. Carter Simple Groups of Lie Type William G. Cochran & Gertrude M. Cox Experimental Designs, Second Edition Richard Courant Differential and Integral Calculus, Volume I Richard Courant Differential and Integral Calculus, Volume II Richard Courant & D. Hilbert Methods of Mathematical Physics, Volume I Richard Courant & D. Hilbert Methods of Mathematical Physics, Volume II D. R. Cox Planning of Experiments Harold S. M. Coxeter Introduction to Geometry, Second Edition Charles W. Curtis & Irving Reiner Representation Theory of Finite Groups and Associative Algebras Charles W. Curtis & Irving Reiner Methods of Representation Theory with Applications to Finite Groups and Orders, Volume I Charles W. Curtis & Irving Reiner Methods of Representation Theory with Applications to Finite Groups and Orders, Volume II Bruno de Finetti Theory of Probability, Volume 1 Bruno de Finetti Theory of Probability, Volume 2 W. Edwards Deming Sample Design in Business Research Amos de Shalit & Herman Feshbach Theoretical Nuclear Physics, Volume 1 - Nuclear Structure J. L. Doob Stochastic Processes Nelson Dunford & Jacob T. Schwartz Linear Operators, Part One, General Theory Nelson Dunford & Jacob T. Schwartz Linear Operators, Part Two, Spectral Theory - Self Adjoint Operators in Hilbert Space Nelson Dunford & Jacob T. Schwartz Linear Operators, Part Three, Spectral Operators Herman Feshbach Theoretical Nuclear Physics: Nuclear Reactions Bernard Friedman Lectures on Applications-Oriented Mathematics Phillip Griffiths & Joseph Harris Principles of Algebraic Geometry Gerald J. Hahn & Samuel S. Shapiro Statistical Models in Engineering Morris H. Hansen, William N. Hurwitz & Willim G. Madow Sample Survey Methods and Theory, Volume I - Methods and Applications Morris H. Hansen, William N. Hurwitz & William G. Madow Sample Survey Methods and Theory, Volume II - Theory Peter Henrici Applied and Computational Complex Analysis, Volume 1 - Power Series - Integration - Conformal Mapping - Location of Zeros Peter Henrici Applied and Computational Complex Analysis, Volume 2 - Special Functions - Integral Transforms - Asymptotics - Continued fractions Peter Henrici Applied and Computational Complex Analysis, Volume 3 - Discrete Fourier Analysis - Cauchy Integrals - Construction of Conformal Maps - Univalent Functions Peter Hilton & Yel-Chiang Wu A Course in Modern Algebra Harry Hochstadt Integral Equations Leslie Kish Survey Sampling Shoshichi Kobayashi & Katsumi Nomizu Foundations of Differential Geometry, Volume 1 Shoshichi Kobayashi & Katsumi Nomizu Foundations of Differential Geometry, Volume 2 Erwin O. Kreyszig Introductory Functional Analysis with Applications William H. Louisell Quantum Statistical Properties of Radiation Ali Hasan Nayfeh Introduction to Perturbation Techniques Ali Hasan Nayfeh & Dean T. Mook Nonlinear Oscillations Emanuel Parzen Modern Probability Theory and Its Applications P. M. Prenter Splines and Variational Methods Walter Rudin Fourier Analysis on Groups I. H. Segal Enzyme Kinetics: Behavior and Analysis of Rapid Equilibrium and Steady-State Enzyme Systems C. L. Siegel Topics in Complex Function Theory, Volume I - Elliptic Functions and Uniformization Theory C. L. Siegel Topics in Complex Function Theory, Volume II - Automorphic and Abelian Integrals C. L. Siegel Topics in Complex Function Theory, Volume III - Abelian Functions and Modular Functions of Several Variables J. J. Stoker Differential Geometry J. J. Stoker Water Waves: The Mathematical Theory with Applications J. J. Stoker Nonlinear Vibrations in Mechanical and Electrical Systems.
(source: Nielsen Book Data)9780471574286 20160528
Law Library (Crown)