1  15
 Yule, G. Udny (George Udny), 18711951.
 11th ed., rev. throughtout and reset.  London : C. Griffin, 1937.
 Description
 Book — xiii, 570 p. : ill. (1 fold.), ill. (part fold.) ; 22 cm.
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311 .Y95 ED.11  Available 
311 .Y95 ED.11  Available 
 Roussas, George G.
 Amsterdam ; Boston : Academic Press, ©2003.
 Description
 Book — 1 online resource (xiii, 523 pages) : illustrations
 Summary

 Chapter 1 Some Motivating Examples and Some; Fundamental Concepts;
 Chapter 2 The Concept of Probability and Some Basic Results;
 Chapter 3 Numerical Characteristics of a Random Variable, Some Special Random Variables;
 Chapter 4 Joint and Conditionalp.d.f:s, Conditional Expectation and Variance, Moment Generating Function, Covariance and Correlation Coefficient;
 Chapter 5 Independence of Random Variables and Some Applications;
 Chapter 6 Transformation of Random Variables;
 Chapter 7 Some Modes of Convergence of Random Variables, Applications;
 Chapter 8 An Overview of Statistical Inference;
 Chapter 9 Point Estimation;
 Chapter 10 Confidence Intervals and Confidence Regions;
 Chapter 11 Testing Hypotheses;
 Chapter 12 More About Testing Hypotheses;
 Chapter 13 A Simple Linear Regression Model;
 Chapter 14 Two Analysis of Variance Models;
 Chapter 15 Some Topics in Nonparametric Inference
 Wackerly, Dennis D., 1945
 7th ed.  Belmont, CA : Thomson Brooks/Cole, c2008.
 Description
 Book — xxii, 912 p. : ill. ; 25 cm.
 Summary

 1. What Is Statistics? Introduction. Characterizing a Set of Measurements: Graphical Methods. Characterizing a Set of Measurements: Numerical Methods. How Inferences Are Made. Theory and Reality. Summary. 2. Probability. Introduction. Probability and Inference. A Review of Set Notation. A Probabilistic Model for an Experiment: The Discrete Case. Calculating the Probability of an Event: The SamplePoint Method. Tools for Counting Sample Points. Conditional Probability and the Independence of Events. Two Laws of Probability. Calculating the Probability of an Event: The EventComposition Methods. The Law of Total Probability and Bayes"s Rule. Numerical Events and Random Variables. Random Sampling. Summary. 3. Discrete Random Variables and Their Probability Distributions. Basic Definition. The Probability Distribution for Discrete Random Variable. The Expected Value of Random Variable or a Function of Random Variable. The Binomial Probability Distribution. The Geometric Probability Distribution. The Negative Binomial Probability Distribution (Optional). The Hypergeometric Probability Distribution. Moments and MomentGenerating Functions. ProbabilityGenerating Functions (Optional). Tchebysheff"s Theorem. Summary. 4. Continuous Random Variables and Their Probability Distributions. Introduction. The Probability Distribution for Continuous Random Variable. The Expected Value for Continuous Random Variable. The Uniform Probability Distribution. The Normal Probability Distribution. The Gamma Probability Distribution. The Beta Probability Distribution. Some General Comments. Other Expected Values. Tchebysheff"s Theorem. Expectations of Discontinuous Functions and Mixed Probability Distributions (Optional). Summary. 5. Multivariate Probability Distributions. Introduction. Bivariate and Multivariate Probability Distributions. Independent Random Variables. The Expected Value of a Function of Random Variables. Special Theorems. The Covariance of Two Random Variables. The Expected Value and Variance of Linear Functions of Random Variables. The Multinomial Probability Distribution. The Bivariate Normal Distribution (Optional). Conditional Expectations. Summary. 6. Functions of Random Variables. Introductions. Finding the Probability Distribution of a Function of Random Variables. The Method of Distribution Functions. The Methods of Transformations. Multivariable Transformations Using Jacobians. Order Statistics. Summary. 7. Sampling Distributions and the Central Limit Theorem. Introduction. Sampling Distributions Related to the Normal Distribution. The Central Limit Theorem. A Proof of the Central Limit Theorem (Optional). The Normal Approximation to the Binomial Distributions. Summary. 8. Estimation. Introduction. The Bias and Mean Square Error of Point Estimators. Some Common Unbiased Point Estimators. Evaluating the Goodness of Point Estimator. Confidence Intervals. LargeSample Confidence Intervals Selecting the Sample Size. SmallSample Confidence Intervals for u and u1u2. Confidence Intervals for o2. Summary. 9. Properties of Point Estimators and Methods of Estimation. Introduction. Relative Efficiency. Consistency. Sufficiency. The RaoBlackwell Theorem and MinimumVariance Unbiased Estimation. The Method of Moments. The Method of Maximum Likelihood. Some LargeSample Properties of MLEs (Optional). Summary. 10. Hypothesis Testing. Introduction. Elements of a Statistical Test. Common LargeSample Tests. Calculating Type II Error Probabilities and Finding the Sample Size for the Z Test. Relationships Between Hypothesis Testing Procedures and Confidence Intervals. Another Way to Report the Results of a Statistical Test: Attained Significance Levels or pValues. Some Comments on the Theory of Hypothesis Testing. SmallSample Hypothesis Testing for u and u1u2. Testing Hypotheses Concerning Variances. Power of Test and the NeymanPearson Lemma. Likelihood Ration Test. Summary. 11. Linear Models and Estimation by Least Squares. Introduction. Linear Statistical Models. The Method of Least Squares. Properties of the Least Squares Estimators for the Simple Linear Regression Model. Inference Concerning the Parameters BI. Inferences Concerning Linear Functions of the Model Parameters: Simple Linear Regression. Predicting a Particular Value of Y Using Simple Linear Regression. Correlation. Some Practical Examples. Fitting the Linear Model by Using Matrices. Properties of the Least Squares Estimators for the Multiple Linear Regression Model. Inferences Concerning Linear Functions of the Model Parameters: Multiple Linear Regression. Prediction a Particular Value of Y Using Multiple Regression. A Test for H0: Bg+1 + Bg+2 = . = Bk = 0. Summary and Concluding Remarks. 12. Considerations in Designing Experiments. The Elements Affecting the Information in a Sample. Designing Experiment to Increase Accuracy. The Matched Pairs Experiment. Some Elementary Experimental Designs. Summary. 13. The Analysis of Variance. Introduction. The Analysis of Variance Procedure. Comparison of More than Two Means: Analysis of Variance for a Oneway Layout. An Analysis of Variance Table for a OneWay Layout. A Statistical Model of the OneWay Layout. Proof of Additivity of the Sums of Squares and E (MST) for a OneWay Layout (Optional). Estimation in the OneWay Layout. A Statistical Model for the Randomized Block Design. The Analysis of Variance for a Randomized Block Design. Estimation in the Randomized Block Design. Selecting the Sample Size. Simultaneous Confidence Intervals for More than One Parameter. Analysis of Variance Using Linear Models. Summary. 14. Analysis of Categorical Data. A Description of the Experiment. The ChiSquare Test. A Test of Hypothesis Concerning Specified Cell Probabilities: A GoodnessofFit Test. Contingency Tables. r x c Tables with Fixed Row or Column Totals. Other Applications. Summary and Concluding Remarks. 15. Nonparametric Statistics. Introduction. A General TwoSampling Shift Model. A Sign Test for a Matched Pairs Experiment. The Wilcoxon SignedRank Test for a Matched Pairs Experiment. The Use of Ranks for Comparing Two Population Distributions: Independent Random Samples. The MannWhitney U Test: Independent Random Samples. The KruskalWallis Test for OneWay Layout. The Friedman Test for Randomized Block Designs. The Runs Test: A Test for Randomness. Rank Correlation Coefficient. Some General Comments on Nonparametric Statistical Test. 16. Introduction to Bayesian Methods for Inference. Introduction. Bayesian Priors, Posteriors and Estimators. Bayesian Credible Intervals. Bayesian Tests of Hypotheses. Summary and Additional Comments. Appendix 1. Matrices and Other Useful Mathematical Results. Matrices and Matrix Algebra. Addition of Matrices. Multiplication of a Matrix by a Real Number. Matrix Multiplication. Identity Elements. The Inverse of a Matrix. The Transpose of a Matrix. A Matrix Expression for a System of Simultaneous Linear Equations. Inverting a Matrix. Solving a System of Simultaneous Linear Equations. Other Useful Mathematical Results. Appendix 2. Common Probability Distributions, Means, Variances, and MomentGenerating Functions. Discrete Distributions. Continuous Distributions. Appendix 3. Tables. Binomial Probabilities. Table of ex. Poisson Probabilities. Normal Curve Areas. Percentage Points of the t Distributions. Percentage Points of the F Distributions. Distribution of Function U. Critical Values of T in the Wilcoxon MatchedPairs, SignedRanks Test. Distribution of the Total Number of Runs R in Sample Size (n1, n2)
 P(R < a). Critical Values of Pearman's Rank Correlation Coefficient. Random Numbers. Answer to Exercises. Index.
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QA276 .M426 2008  Available 
5. Correlation and dependence [2001]
 Mari, Dominique Drouet.
 London : Imperial College Press ; River Edge, NJ : Distributed by World Scientific Pub. Co., ©2001.
 Description
 Book — 1 online resource (xiv, 219 pages) : illustrations Digital: data file.
 Summary

 Notations and definitions
 correlation and dependence  an introspection
 concepts of dependence and stochastic ordering
 copulas
 FarlieGumbelMorgenstern models of dependence
 global versus local dependence between random variables.
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 Cambridge ; New York : Cambridge University Press, 2002.
 Description
 Book — 1 online resource (ix, 302 pages) Digital: data file.
 Summary

 The enigma of simplicity / Hugo A. Keuzenkamp, Michael McAleer and Arnold Zellner
 What is the problem of simplicity? / Elliott Sober
 Science seeks parsimony, not simplicity : searching for pattern in phenomena / Herbert A. Simon
 A macroeconomic approach to complexity / Marcel Boumans
 The new science of simplicity / Malcolm R. Forster
 What explains complexity? / Bert Hamminga
 Occam's bonus / A.W.F. Edwards
 Simplicity, information, Kolmogorov complexity and prediction / Paul Vitányi and Ming Li
 Simplicity and statistical inference / Jorma Rissanen
 Rissanen's theorem and econometric time series / Werner Ploberger and Peter C.B. Phillips
 Parametric versus nonparametric inference : statistical models and simplicity / Aris Spanos
 The role of simplicity in an econometric model selection process / Antonio Aznar, M. Isabel Ayuda and Carmen GarcíaOlaverri
 Simplicity in a behavioural, nonparametric context / Dirk Tempelaar
 Keep it sophisticatedly simple / Arnold Zellner
 Communication, complexity and coordination in games / Mattias Ganslandt
 The simplicity of an earnings frontier / Uwe Jensen
 Simplicity : views of some Nobel laureates in economic science / Michael McAleer.
 New York : Springer, ©1996.
 Description
 Book — 1 online resource (xvi, 448 pages) : illustrations Digital: text file.PDF.
 Summary

Modelling and Prediction Honoring Seymour Geisser contains the refereed proceedings of the Conference on Forecasting, Prediction, and Modelling held at National Chiao Tung University, Taiwan in 1994. The papers discuss general methodological issues; prediction; design of experiments and classification; prior distributions and estimation; posterior odds, testing, and model selection; modelling and prediction in finance; and time series modelling and applications. Specific topics include very interesting and topical statistical issues related to DNA fingerprinting and spatial image reconstruction, foundational issues for applied statistics and testing hypotheses, forecasting tax revenues and bond prices, and assessing oxone depletion.
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8. Applied smoothing techniques for data analysis : the kernel approach with SPlus illustrations [1997]
 Bowman, A. W.
 Oxford : Clarendon Press ; New York : Oxford University Press, 1997.
 Description
 Book — 1 online resource (xi, 193 pages) : illustrations
 Summary

 1. Density estimation for exploring data
 2. Density estimation for inference
 3. Nonparametric regression for exploring data
 4. Inference with nonparametric regression
 5. Checking parametric regression models
 6. Comparing regression curves and surfaces
 7. Time series data
 8. An introduction to semiparametric and additive models
 References.
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 Morgan, Stephen L. (Stephen Lawrence), 1971
 New York : Cambridge University Press, 2007.
 Description
 Book — 1 online resource (xiii, 319 pages) : illustrations
 Summary

 Part I. Counterfactual Causality and Empirical Research in the Social Sciences: 1. Introduction
 2. The counterfactual model
 Part II. Estimating Causal Effects by Conditioning: 3. Causal graphs, identification, and models of causal exposure
 4. Matching estimators of causal effects
 5. Regression estimators of causal effects
 Part III. Estimating Causal Effects When Simple Conditioning Is Ineffective: 6. Identification in the absence of a complete model of causal exposure
 7. Natural experiments and instrumental variables
 8. Mechanisms and causal explanation
 9. Repeated observations and the estimation of causal effects
 Part IV. Conclusions: 10. Counterfactual causality and future empirical research in the social sciences.
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10. Statistical inference [2002]
 Casella, George.
 2nd ed.  Australia ; Pacific Grove, CA : Thomson Learning, ©2002.
 Description
 Book — xxviii, 660 pages : ill. ; 25 cm.
 Summary

 Probability theory
 Transformations and expectations
 Common families of distributions
 Multiple random variables
 Properties of a random sample
 Principles of data reduction
 Point estimation
 Hypothesis testing
 Interval estimation
 Asymptotic evaluations
 Analysis of variance and regression
 Regression models.
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QA276 .C37 2002  Unknown 
11. Generalized method of moments [2005]
 Hall, Alastair R.
 Oxford ; New York : Oxford University Press, 2005.
 Description
 Book — 1 online resource (xii, 400 pages) : illustrations, tables Digital: data file.
 Summary

 1. Introduction
 2. The Instrumental Variable Estimator in the Linear Regression Model
 3. GMM Estimation in Correctly Specified Models
 4. GMM Estimation in Misspecified Models
 5. Hypothesis Testing
 6. Asymptotic Theory and Finite Sample Behaviour
 7. Moment Selection in Theory and in Practice
 8. Alternative Approximations in Finite Sample Behaviour
 9. Empirical Examples
 10. Related Methods of Estimation
 Appendix: Mixing processes and Nonstationarity.
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This book has become one of the main statistical tools for the analysis of economic and financial data. Designed for both theoreticians and practitioners, this book provides a comprehensive treatment of GMM estimation and inference. All the main statistical results are discussed intuitively and proved formally, and all the inference techniques are illustrated using empirical examples in macroeconomics and finance. This book is the first to provide an intuitive introduction to the method combined with a unified treatment of GMM statistical theory and a survey of recent important developments in the field. About the Series Advanced Texts in Econometrics is a distinguished and rapidly expanding series in which leading econometricians assess recent developments in such areas as stochastic probability, panel and time series data analysis, modeling, and cointegration. In both hardback and affordable paperback, each volume explains the nature and applicability of a topic in greater depth than possible in introductory textbooks or single journal articles. Each definitive work is formatted to be as accessible and convenient for those who are not familiar with the detailed primary literature.
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 Mayo, Deborah G.
 Chicago : University of Chicago Press, 1996.
 Description
 Book — 1 online resource (xvi, 493 pages) : illustrations
 Summary

 Preface
 1: Learning from Error
 2: Ducks, Rabbits, and Normal Science: Recasting the Kuhn'sEye View of Popper
 3: The New Experimentalism and the Bayesian Way
 4: Duhem, Kuhn, and Bayes
 5: Models of Experimental Inquiry
 6: Severe Tests and Methodological Underdetermination
 7: The Experimental Basis from Which to Test Hypotheses: Brownian Motion
 8: Severe Tests and Novel Evidence
 9: Hunting and Snooping: Understanding the NeymanPearson Predesignationist Stance
 10: Why You Cannot Be Just a Little Bit Bayesian
 11: Why Pearson Rejected the NeymanPearson (Behavioristic) Philosophy and a Note on Objectivity in Statistics
 12: Error Statistics and Peircean Error Correction
 13: Toward an ErrorStatistical Philosophy of Science References Index.
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 Johansen, Søren, 1939
 Oxford ; New York : Oxford University Press, 1995.
 Description
 Book — 1 online resource (x, 267 pages) : illustrations
 Summary

 pt. I. The Statistical Analysis of Cointegration. 1. Introduction. 2. The Vector Autoregressive Model. 3. Basic Definitions and Concepts. 4. Cointegration and Representation of Integrated Variables. 5. The I(1) Models and their Interpretation. 6. The Statistical Analysis of I(1) Models. 7. Hypothesis Testing for the LongRun Coefficients [beta]. 8. Partial Systems and Hypotheses on [alpha]. 9. The I(2) Model and a Test for I(2)
 pt. II. The Probability Analysis of Cointegration. 10. Probability Properties of I(1) Processes. 11. The Asymptotic Distribution of the Test for Cointegrating Rank. 12. Determination of Cointegrating Rank. 13. Asymptotic Properties of the Estimators. 14. The Power Function of the Test for Cointegrating Rank under Local Alternatives. 15. Simulations and Tables
 App. A. Some Mathematical Results
 App. B. Weak Convergence of Probability Measures on R[superscript P] and C[0,1].
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Professor Johansen, a leading statistician working in econometrics, gives a detailed mathematical and statistical analysis of the cointegrated vector autoregressive model, which has been gaining in popularity. The book is a selfcontained presentation for graduate students and researchers with a good knowledge of multivariate regression analysis and likelihood methods. The theoretical analysis is illustrated with the empirical analysis of two sets of economic data. The theory has been developed in close contact with the application and the methods have been implemented in the computer package CATS in RATS. ; This book gives a detailed mathematical and statistical analysis of the cointegrated vector autoregresive model. This model had gained popularity because it can at the same time capture the shortrun dynamic properties as well as the longrun equilibrium behaviour of many nonstationary time series. It also allows relevant economic questions to be formulated in a consistent statistical framework.The book is intended to give a relatively selfcontained presentation for graduate students and researchers with a good knowledge of multivariate regression analysis and likelihood methods. The asymptotic theory requires some familiarity with the theory of weak convergence of stochastic processes. The theory is treated in detail with the purpose of giving the reader a working knowledge of the techniques involved.
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 Johansen, Søren, 1939
 Oxford ; New York : Oxford University Press, 1995.
 Description
 Book — 1 online resource (x, 267 pages) : illustrations
 Summary

 pt. I. The Statistical Analysis of Cointegration. 1. Introduction. 2. The Vector Autoregressive Model. 3. Basic Definitions and Concepts. 4. Cointegration and Representation of Integrated Variables. 5. The I(1) Models and their Interpretation. 6. The Statistical Analysis of I(1) Models. 7. Hypothesis Testing for the LongRun Coefficients [beta]. 8. Partial Systems and Hypotheses on [alpha]. 9. The I(2) Model and a Test for I(2)
 pt. II. The Probability Analysis of Cointegration. 10. Probability Properties of I(1) Processes. 11. The Asymptotic Distribution of the Test for Cointegrating Rank. 12. Determination of Cointegrating Rank. 13. Asymptotic Properties of the Estimators. 14. The Power Function of the Test for Cointegrating Rank under Local Alternatives. 15. Simulations and Tables
 App. A. Some Mathematical Results
 App. B. Weak Convergence of Probability Measures on R[superscript P] and C[0,1].
(source: Nielsen Book Data)
Professor Johansen, a leading statistician working in econometrics, gives a detailed mathematical and statistical analysis of the cointegrated vector autoregressive model, which has been gaining in popularity. The book is a selfcontained presentation for graduate students and researchers with a good knowledge of multivariate regression analysis and likelihood methods. The theoretical analysis is illustrated with the empirical analysis of two sets of economic data. The theory has been developed in close contact with the application and the methods have been implemented in the computer package CATS in RATS. ; This book gives a detailed mathematical and statistical analysis of the cointegrated vector autoregresive model. This model had gained popularity because it can at the same time capture the shortrun dynamic properties as well as the longrun equilibrium behaviour of many nonstationary time series. It also allows relevant economic questions to be formulated in a consistent statistical framework.The book is intended to give a relatively selfcontained presentation for graduate students and researchers with a good knowledge of multivariate regression analysis and likelihood methods. The asymptotic theory requires some familiarity with the theory of weak convergence of stochastic processes. The theory is treated in detail with the purpose of giving the reader a working knowledge of the techniques involved.
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 Hastie, Trevor author.
 Second edition.  New York : Springer, [2009]
 Description
 Book — xxii, 745 pages : illustrations (some color), charts ; 24 cm.
 Summary

 1. Introduction
 2. Overview of supervised learning
 3. Linear methods for regression
 4. Linear methods for classification
 5. Basis expansions and regularization
 6. Kernel smoothing methods
 7. Model assessment and selection
 8. Model inference and averaging
 9. Additive models, trees, and related methods
 10. Boosting and additive trees
 11. Neural networks
 12. Support vector machines and flexible discriminants
 13. Prototype methods and nearestneighbors
 14. Unsupervised learning
 15. Random forests
 16. Ensemble learning
 17. Undirected graphical models
 18. Highdimensional problems: p>> N.
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Q325.75 .H37 2009  Unknown 
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