Bayesian Non- and Semi-parametric Methods and Applications

Peter Rossi

This book reviews and develops Bayesian non-parametric and semi-parametric methods for applications in microeconometrics and quantitative marketing. Most econometric models used in microeconomics and marketing applications involve arbitrary distributional assumptions. As more data becomes available, a natural desire to provide methods that relax these assumptions arises. Peter Rossi advocates a Bayesian approach in which specific distributional assumptions are replaced with more flexible distributions based on mixtures of normals. The Bayesian approach can use either a large but fixed number

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Year of publication:	2014
Authors:	Rossi, Peter E.
Publisher:	Princeton : Princeton University Press
Subject:	Bayes-Statistik \| Bayesian inference \| Nichtparametrisches Verfahren \| Nonparametric statistics \| Theorie \| Theory \| Bayes-Entscheidungstheorie
Description of contents:	Table of Contents [external.dandelon.com] ; Description [degruyter.com] ; Description [degruyter.com] ; Description [zbmath.org] ; Description [degruyter.com]

Extent:	Online-Ressource (219 p)
Series:	The Econometric and Tinbergen Institutes Lectures
Type of publication:	Book / Working Paper
Language:	English
Notes:	Description based upon print version of record Cover; Title; Copyright; Contents; Preface; 1 Mixtures of Normals; 1.1 Finite Mixture of Normals Likelihood Function; 1.2 Maximum Likelihood Estimation; 1.3 Bayesian Inference for the Mixture of Normals Model; 1.4 Priors and the Bayesian Model; 1.5 Unconstrained Gibbs Sampler; 1.6 Label-Switching; 1.7 Examples; 1.8 Clustering Observations; 1.9 Marginalized Samplers; 2 Dirichlet Process Prior and Density Estimation; 2.1 Dirichlet Processes-A Construction; 2.2 Finite and Infinite Mixture Models; 2.3 Stick-Breaking Representation; 2.4 Polya Urn Representation and Associated Gibbs Sampler 2.5 Priors on DP Parameters and Hyper-parameters2.6 Gibbs Sampler for DP Models and Density Estimation; 2.7 Scaling the Data; 2.8 Density Estimation Examples; 3 Non-parametric Regression; 3.1 Joint vs. Conditional Density Approaches; 3.2 Implementing the Joint Approach with Mixtures of Normals; 3.3 Examples of Non-parametric Regression Using Joint Approach; 3.4 Discrete Dependent Variables; 3.5 An Example of Expenditure Function Estimation; 4 Semi-parametric Approaches; 4.1 Semi-parametric Regression with DP Priors; 4.2 Semi-parametric IV Models; 5 Random Coefficient Models; 5.1 Introduction 5.2 Semi-parametric Random Coefficient Logit Models5.3 An Empirical Example of a Semi-parametric Random Coefficient Logit Model; 6 Conclusions and Directions for Future Research; 6.1 When Are Non-parametric and Semi-parametric Methods Most Useful?; 6.2 Semi-parametric or Non-parametric Methods?; 6.3 Extensions; Bibliography; Index In English
ISBN:	978-1-4008-5030-3 ; 978-0-691-14532-7 ; 978-0-691-14532-7
Other identifiers:	10.1515/9781400850303 [DOI]
Source:	ECONIS - Online Catalogue of the ZBW

Persistent link: https://ebvufind01.dmz1.zbw.eu/10014481724