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Bayesian Non- and Semi-parametric Methods and Applications
Peter E. Rossi

Book Description | Endorsements
Preface [in PDF format] | Chapter 1 [in PDF format]

TABLE OF CONTENTS:

Preface vii
1 Mixtures of Normals 1
1.1 Finite Mixture of Normals Likelihood Function 6
1.2 Maximum Likelihood Estimation 9
1.3 Bayesian Inference for the Mixture of Normals Model 15
1.4 Priors and the Bayesian Model 16
1.5 Unconstrained Gibbs Sampler 25
1.6 Label-Switching 29
1.7 Examples 34
1.8 Clustering Observations 46
1.9 Marginalized Samplers 49
2 Dirichlet Process Prior and Density Estimation 59
2.1 Dirichlet Processes--A Construction 60
2.2 Finite and Infinite Mixture Models 64
2.3 Stick-Breaking Representation 68
2.4 Polya Urn Representation and Associated Gibbs Sampler 70
2.5 Priors on DP Parameters and Hyper-parameters 72
2.6 Gibbs Sampler for DP Models and Density Estimation 78
2.7 Scaling the Data 80
2.8 Density Estimation Examples 81
3 Non-parametric Regression 90
3.1 Joint vs. Conditional Density Approaches 90
3.2 Implementing the Joint Approach with Mixtures of Normals 93
3.3 Examples of Non-parametric Regression Using Joint Approach 96
3.4 Discrete Dependent Variables 104
3.5 An Example of Expenditure Function Estimation 108
4 Semi-parametric Approaches 115
4.1 Semi-parametric Regression with DP Priors 115
4.2 Semi-parametric IV Models 122
5 Random Coefficient Models 152
5.1 Introduction 152
5.2 Semi-parametric Random Coefficient Logit Models 157
5.3 An Empirical Example of a Semi-parametric Random Coefficient Logit Model 161
6 Conclusions and Directions for Future Research 187
6.1 When Are Non-parametric and Semi-parametric Methods Most Useful? 187
6.2 Semi-parametric or Non-parametric Methods? 189
6.3 Extensions 191
Bibliography 195
Index 201

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File created: 7/11/2014

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