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Monte Carlo Statistical Methods

  • Christian P. Robert
  • George Casella

Part of the Springer Texts in Statistics book series (STS)

Table of contents

  1. Front Matter
    Pages i-xxi
  2. Christian P. Robert, George Casella
    Pages 1-34
  3. Christian P. Robert, George Casella
    Pages 35-70
  4. Christian P. Robert, George Casella
    Pages 71-138
  5. Christian P. Robert, George Casella
    Pages 139-191
  6. Christian P. Robert, George Casella
    Pages 193-230
  7. Christian P. Robert, George Casella
    Pages 231-283
  8. Christian P. Robert, George Casella
    Pages 285-361
  9. Christian P. Robert, George Casella
    Pages 363-413
  10. Christian P. Robert, George Casella
    Pages 415-450
  11. Back Matter
    Pages 451-509

About this book

Introduction

Monte Carlo statistical methods, particularly those based on Markov chains, have now matured to be part of the standard set of techniques used by statisticians. This book is intended to bring these techniques into the class­ room, being (we hope) a self-contained logical development of the subject, with all concepts being explained in detail, and all theorems, etc. having detailed proofs. There is also an abundance of examples and problems, re­ lating the concepts with statistical practice and enhancing primarily the application of simulation techniques to statistical problems of various dif­ ficulties. This is a textbook intended for a second-year graduate course. We do not assume that the reader has any familiarity with Monte Carlo techniques (such as random variable generation) or with any Markov chain theory. We do assume that the reader has had a first course in statistical theory at the level of Statistical Inference by Casella and Berger (1990). Unfortu­ nately, a few times throughout the book a somewhat more advanced no­ tion is needed. We have kept these incidents to a minimum and have posted warnings when they occur. While this is a book on simulation, whose actual implementation must be processed through a computer, no requirement is made on programming skills or computing abilities: algorithms are pre­ sented in a program-like format but in plain text rather than in a specific programming language. (Most of the examples in the book were actually implemented in C, with the S-Plus graphical interface.

Keywords

MCMCM Markov Chain Monte Carlo Methods Monte Carlo method mathematical statistics random variable statistical inference statistics

Authors and affiliations

  • Christian P. Robert
    • 1
    • 2
  • George Casella
    • 3
  1. 1.Laboratoire de StatistiqueCREST-INSEEParis Cedex 14France
  2. 2.Dept. de Mathematique UFR des SciencesUniversite de RouenMont Saint Aignan cedexFrance
  3. 3.Biometrics UnitCornell UniversityIthacaUSA

Bibliographic information

  • DOI https://doi.org/10.1007/978-1-4757-3071-5
  • Copyright Information Springer-Verlag New York 1999
  • Publisher Name Springer, New York, NY
  • eBook Packages Springer Book Archive
  • Print ISBN 978-1-4757-3073-9
  • Online ISBN 978-1-4757-3071-5
  • Series Print ISSN 1431-875X
  • Buy this book on publisher's site