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Tools for Statistical Inference

Observed Data and Data Augmentation Methods

  • Martin A. Tanner

Part of the Lecture Notes in Statistics book series (LNS, volume 67)

Table of contents

  1. Front Matter
    Pages I-VI
  2. Martin A. Tanner
    Pages 1-5
  3. Martin A. Tanner
    Pages 6-15
  4. Martin A. Tanner
    Pages 30-46
  5. Martin A. Tanner
    Pages 47-88
  6. Martin A. Tanner
    Pages 89-107
  7. Back Matter
    Pages 108-113

About this book

Introduction

From the reviews: The purpose of the book under review is to give a survey of methods for the Bayesian or likelihood-based analysis of data. The author distinguishes between two types of methods: the observed data methods and the data augmentation ones. The observed data methods are applied directly to the likelihood or posterior density of the observed data. The data augmentation methods make use of the special "missing" data structure of the problem. They rely on an augmentation of the data which simplifies the likelihood or posterior density. #Zentralblatt für Mathematik#

Keywords

Monte Carlo method expectation–maximization algorithm generalized linear model latent class analysis likelihood statistical inference

Authors and affiliations

  • Martin A. Tanner
    • 1
  1. 1.Department of BiostatisticsUniversity of Rochester Medical CenterRochesterUSA

Bibliographic information

  • DOI https://doi.org/10.1007/978-1-4684-0510-1
  • Copyright Information Springer-Verlag New York 1991
  • Publisher Name Springer, New York, NY
  • eBook Packages Springer Book Archive
  • Print ISBN 978-0-387-97525-2
  • Online ISBN 978-1-4684-0510-1
  • Series Print ISSN 0930-0325
  • Buy this book on publisher's site