دانلود کتاب Bayesian and Frequentist Regression Methods

This book provides a balanced, modern summary of Bayesian and frequentist methods for regression analysis.Table of ContentsCoverBayesian and Frequentist Regression MethodsISBN 9781441909244 ISBN 9781441909251PrefaceContentsChapter 1 Introduction and Motivating Examples 1.1 Introduction 1.2 Model Formulation 1.3 Motivating Examples 1.3.1 Prostate Cancer 1.3.2 Outcome After Head Injury 1.3.3 Lung Cancer and Radon 1.3.4 Pharmacokinetic Data 1.3.5 Dental Growth 1.3.6 Spinal Bone Mineral Density 1.4 Nature of Randomness 1.5 Bayesian and Frequentist Inference 1.6 The Executive Summary 1.7 Bibliographic NotesPart I Chapter 2 Frequentist Inference 2.1 Introduction 2.2 Frequentist Criteria 2.3 Estimating Functions 2.4 Likelihood o 2.4.1 Maximum Likelihood Estimation o 2.4.2 Variants on Likelihood o 2.4.3 Model Misspecification 2.5 Quasi-likelihood 2.5.1 Maximum Quasi-likelihood Estimation o 2.5.2 A More Complex Mean-Variance Model 2.6 Sandwich Estimation 2.7 Bootstrap Methods o 2.7.1 The Bootstrap for a Univariate Parameter o 2.7.2 The Bootstrap for Regression o 2.7.3 Sandwich Estimation and the Bootstrap 2.8 Choice of Estimating Function 2.9 Hypothesis Testing o 2.9.1 Motivation o 2.9.2 Preliminaries o 2.9.3 Score Tests o 2.9.4 Wald Tests o 2.9.5 Likelihood Ratio Tests o 2.9.6 Quasi-likelihood o 2.9.7 Comparison of Test Statistics 2.10 Concluding Remarks 2.11 Bibliographic Notes 2.12 Exercises Chapter 3 Bayesian Inference 3.1 Introduction 3.2 The Posterior Distribution and Its Summarization 3.3 Asymptotic Properties of Bayesian Estimators 3.4 Prior Choice o 3.4.1 Baseline Priors o 3.4.2 Substantive Priors o 3.4.3 Priors on Meaningful Scales o 3.4.4 Frequentist Considerations 3.5 Model Misspecification 3.6 Bayesian Model Averaging 3.7 Implementation o 3.7.1 Conjugacy o 3.7.2 Laplace Approximation o 3.7.3 Quadrature o 3.7.4 Integrated Nested Laplace Approximations o 3.7.5 Importance Sampling Monte Carlo o 3.7.6 Direct Sampling Using Conjugacy o 3.7.7 Direct Sampling Using the Rejection Algorithm 3.8 Markov Chain Monte Carlo 3.8.1 Markov Chains for Exploring Posterior Distributions o 3.8.2 The Metropolis-Hastings Algorithm o 3.8.3 The Metropolis Algorithm o 3.8.4 The Gibbs Sampler o 3.8.5 Combining Markov Kernels: Hybrid Schemes o 3.8.6 Implementation Details o 3.8.7 Implementation Summary 3.9 Exchangeability 3.10 Hypothesis Testing with Bayes Factors 3.11 Bayesian Inference Based on a Sampling Distribution 3.12 Concluding Remarks 3.13 Bibliographic Notes 3.14 Exercises Chapter 4 Hypothesis Testing and Variable Selection 4.1 Introduction 4.2 Frequentist Hypothesis Testing o 4.2.1 Fisherian Approach o 4.2.2 Neyman-Pearson Approach o 4.2.3 Critique of the Fisherian Approach o 4.2.4 Critique of the Neyman-Pearson Approach 4.3 Bayesian Hypothesis Testing with Bayes Factors 4.3.1 Overview of Approaches o 4.3.2 Critique of the Bayes Factor Approach o 4.3.3 A Bayesian View of Frequentist Hypothesis Testing 4.4 The Jeffreys-Lindley Paradox 4.5 Testing Multiple Hypotheses: General Considerations 4.6 Testing Multiple Hypotheses: Fixed Number of Tests o 4.6.1 Frequentist Analysis o 4.6.2 Bayesian Analysis 4.7 Testing Multiple Hypotheses: Variable Selection 4.8 Approaches to Variable Selection and Modeling o 4.8.1 Stepwise Methods o 4.8.2 All Possible Subsets o 4.8.3 Bayesian Model Averaging o 4.8.4 Shrinkage Methods 4.9 Model Building Uncertainty 4.10 A Pragmatic Compromise to Variable Selection 4.11 Concluding Comments 4.12 Bibliographic Notes 4.13 ExercisesPart II Chapter 5 Linear Models 5.1 Introduction 5.2 Motivating Example: Prostate Cancer 5.3 Model Specifiation 5.4 A Justificatio for Linear Modeling 5.5 Parameter Interpretation o 5.5.1 Causation Versus Association o 5.5.2 Multiple Parameters o 5.5.3 Data Transformations 5.6 Frequentist Inference 5.6.1 Likelihood o 5.6.2 Least Squares Estimation o 5.6.3 The Gauss-Markov Theorem o 5.6.4 Sandwich Estimation 5.7 Bayesian Inference 5.8 Analysis of Variance o 5.8.1 One-Way ANOVA o 5.8.2 Crossed Designs o 5.8.3 Nested Designs o 5.8.4 Random and Mixed Effects Models 5.9 Bias-Variance Trade-Off 5.10 Robustness to Assumptions o 5.10.1 Distribution of Errors o 5.10.2 Nonconstant Variance o 5.10.3 Correlated Errors 5.11 Assessment of Assumptions o 5.11.1 Review of Assumptions o 5.11.2 Residuals and In uence o 5.11.3 Using the Residuals 5.12 Example: Prostate Cancer 5.13 Concluding Remarks 5.14 Bibliographic Notes 5.15 Exercises Chapter 6 General Regression Models 6.1 Introduction 6.2 Motivating Example: Pharmacokinetics of Theophylline 6.3 Generalized Linear Models 6.4 Parameter Interpretation 6.5 Likelihood Inference for GLMs 6.5.1 Estimation o 6.5.2 Computation o 6.5.3 Hypothesis Testing 6.6 Quasi-likelihood Inference for GLMs 6.7 Sandwich Estimation for GLMs 6.8 Bayesian Inference for GLMs o 6.8.1 Prior Specification o 6.8.2 Computation o 6.8.3 Hypothesis Testing o 6.8.4 Overdispersed GLMs 6.9 Assessment of Assumptions for GLMs 6.10 Nonlinear Regression Models 6.11 Identifiabilit 6.12 Likelihood Inference for Nonlinear Models 6.12.1 Estimation o 6.12.2 Hypothesis Testing 6.13 Least Squares Inference 6.14 Sandwich Estimation for Nonlinear Models 6.15 The Geometry of Least Squares 6.16 Bayesian Inference for Nonlinear Models o 6.16.1 Prior Specification o 6.16.2 Computation o 6.16.3 Hypothesis Testing 6.17 Assessment of Assumptions for Nonlinear Models 6.18 Concluding Remarks 6.19 Bibliographic Notes 6.20 Exercises Chapter 7 Binary Data Models 7.1 Introduction 7.2 Motivating Examples 7.2.1 Outcome After Head Injury o 7.2.2 Aircraft Fasteners o 7.2.3 Bronchopulmonary Dysplasia 7.3 The Binomial Distribution 7.3.1 Genesis o 7.3.2 Rare Events 7.4 Generalized Linear Models for Binary Data 7.4.1 Formulation o 7.4.2 Link Functions 7.5 Overdispersion 7.6 Logistic Regression Models 7.6.1 Parameter Interpretation o 7.6.2 Likelihood Inference for Logistic Regression Models o 7.6.3 Quasi-likelihood Inference for Logistic Regression Models o 7.6.4 Bayesian Inference for Logistic Regression Models 7.7 Conditional Likelihood Inference 7.8 Assessment of Assumptions 7.9 Bias, Variance, and Collapsibility 7.10 Case-Control Studies o 7.10.1 The Epidemiological Context o 7.10.2 Estimation for a Case-Control Study o 7.10.3 Estimation for a Matched Case-Control Study 7.11 Concluding Remarks 7.12 Bibliographic Notes 7.13 ExercisesPart III Chapter 8 Linear Models 8.1 Introduction 8.2 Motivating Example: Dental Growth Curves 8.3 The Effciency of Longitudinal Designs 8.4 Linear Mixed Models 8.4.1 The General Framework o 8.4.2 Covariance Models for Clustered Data o 8.4.3 Parameter Interpretation for Linear Mixed Models 8.5 Likelihood Inference for Linear Mixed Models o 8.5.1 Inference for Fixed Effects o 8.5.2 Inference for Variance Components via Maximum Likelihood o 8.5.3 Inference for Variance Components via Restricted Maximum Likelihood o 8.5.4 Inference for Random Effects 8.6 Bayesian Inference for Linear Mixed Models 8.6.1 A Three-Stage Hierarchical Model o 8.6.2 Hyperpriors o 8.6.3 Implementation o 8.6.4 Extensions 8.7 Generalized Estimating Equations 8.7.1 Motivation o 8.7.2 The GEE Algorithm o 8.7.3 Estimation of Variance Parameters 8.8 Assessment of Assumptions 8.8.1 Review of Assumptions o 8.8.2 Approaches to Assessment 8.9 Cohort and Longitudinal Effects 8.10 Concluding Remarks 8.11 Bibliographic Notes 8.12 Exercises Chapter 9 General Regression Models 9.1 Introduction 9.2 Motivating Examples o 9.2.1 Contraception Data o 9.2.2 Seizure Data o 9.2.3 Pharmacokinetics of Theophylline 9.3 Generalized Linear Mixed Models 9.4 Likelihood Inference for Generalized Linear Mixed Models 9.5 Conditional Likelihood Inference for Generalized Linear Mixed Models 9.6 Bayesian Inference for Generalized Linear Mixed Models 9.6.1 Model Formulation o 9.6.2 Hyperpriors 9.7 Generalized Linear Mixed Models with Spatial Dependence 9.7.1 A Markov Random Field Prior o 9.7.2 Hyperpriors 9.8 Conjugate Random Effects Models 9.9 Generalized Estimating Equations for Generalized Linear Models 9.10 GEE2: Connected Estimating Equations 9.11 Interpretation of Marginal and Conditional Regression Coeffiients 9.12 Introduction to Modeling Dependent Binary Data 9.13 Mixed Models for Binary Data 9.13.1 Generalized Linear Mixed Models for Binary Data o 9.13.2 Likelihood Inference for the Binary Mixed Model o 9.13.3 Bayesian Inference for the Binary Mixed Model o 9.13.4 Conditional Likelihood Inference for Binary Mixed Models 9.14 Marginal Models for Dependent Binary Data o 9.14.1 Generalized Estimating Equations o 9.14.2 Loglinear Models o 9.14.3 Further Multivariate Binary Models 9.15 Nonlinear Mixed Models 9.16 Parameterization of the Nonlinear Model 9.17 Likelihood Inference for the Nonlinear Mixed Model 9.18 Bayesian Inference for the Nonlinear Mixed Model o 9.18.1 Hyperpriors o 9.18.2 Inference for Functions of Interest 9.19 Generalized Estimating Equations 9.20 Assessment of Assumptions for General Regression Models 9.21 Concluding Remarks 9.22 Bibliographic Notes 9.23 ExercisesPart IV Chapter 10 Preliminaries for Nonparametric Regression 10.1 Introduction 10.2 Motivating Examples o 10.2.1 Light Detection and Ranging o 10.2.2 Ethanol Data 10.3 The Optimal Prediction o 10.3.1 Continuous Responses o 10.3.2 Discrete Responses with K Categories o 10.3.3 General Responses o 10.3.4 In Practice 10.4 Measures of Predictive Accuracy o 10.4.1 Continuous Responses o 10.4.2 Discrete Responses with K Categories o 10.4.3 General Responses 10.5 A First Look at Shrinkage Methods o 10.5.1 Ridge Regression o 10.5.2 The Lasso 10.6 Smoothing Parameter Selection o 10.6.1 Mallows CP o 10.6.2 K-Fold Cross-Validation o 10.6.3 Generalized Cross-Validation o 10.6.4 AIC for General Models o 10.6.5 Cross-Validation for Generalized Linear Models 10.7 Concluding Comments 10.8 Bibliographic Notes 10.9 Exercises Chapter 11 Spline and Kernel Methods 11.1 Introduction 11.2 Spline Methods 11.2.1 Piecewise Polynomials and Splines o 11.2.2 Natural Cubic Splines o 11.2.3 Cubic Smoothing Splines o 11.2.4 B-Splines o 11.2.5 Penalized Regression Splines o 11.2.6 A Brief Spline Summary o 11.2.7 Inference for Linear Smoothers o 11.2.8 Linear Mixed Model Spline Representation: Likelihood Inference o 11.2.9 Linear Mixed Model Spline Representation: Bayesian Inference 11.3 Kernel Methods o 11.3.1 Kernels o 11.3.2 Kernel Density Estimation o 11.3.3 The Nadaraya-Watson Kernel Estimator o 11.3.4 Local Polynomial Regression 11.4 Variance Estimation 11.5 Spline and Kernel Methods for Generalized Linear Models o 11.5.1 Generalized Linear Models with Penalized Regression Splines o 11.5.2 A Generalized Linear Mixed Model Spline Representation o 11.5.3 Generalized Linear Models with Local Polynomials 11.6 Concluding Comments 11.7 Bibliographic Notes 11.8 Exercises Chapter 12 Nonparametric Regression with Multiple Predictors 12.1 Introduction 12.2 Generalized Additive Models 12.2.1 Model Formulation o 12.2.2 Computation via Backfittin 12.3 Spline Methods with Multiple Predictors o 12.3.1 Natural Thin Plate Splines o 12.3.2 Thin Plate Regression Splines o 12.3.3 Tensor Product Splines 12.4 Kernel Methods with Multiple Predictors 12.5 Smoothing Parameter Estimation 12.5.1 Conventional Approaches o 12.5.2 Mixed Model Formulation 12.6 Varying-Coefficien Models 12.7 Regression Trees 12.7.1 Hierarchical Partitioning o 12.7.2 Multiple Adaptive Regression Splines 12.8 Classificatio o 12.8.1 Logistic Models with K Classes o 12.8.2 Linear and Quadratic Discriminant Analysis o 12.8.3 Kernel Density Estimation and Classificatio o 12.8.4 Classificatio Trees o 12.8.5 Bagging o 12.8.6 Random Forests 12.9 Concluding Comments 12.10 Bibliographic Notes 12.11 ExercisesPart V Appendix A Differentiation of Matrix Expressions Appendix B Matrix Results Appendix C Some Linear Algebra Appendix D Probability Distributions and Generating Functions Appendix E Functions of Normal Random Variables Appendix F Some Results from Classical Statistics Appendix G Basic Large Sample TheoryReferencesIndex