Advertisement

Empirical Bayes and Likelihood Inference

  • S. E. Ahmed
  • N. Reid

Part of the Lecture Notes in Statistics book series (LNS, volume 148)

About this book

Introduction

Bayesian and likelihood approaches to inference have a number of points of close contact, especially from an asymptotic point of view. Both approaches emphasize the construction of interval estimates of unknown parameters. Empirical Bayes methods have historically emphasized instead the construction of point estimates. In this volume researchers present recent work on several aspects of Bayesian, likelihood and empirical Bayes methods, presented at a workshop held in Montreal, Canada. The goal of the workshop was to explore the linkages among the methods, and to suggest new directions for research in the theory of inference.

Keywords

Estimator Likelihood Variance algorithms analysis of variance expectation–maximization algorithm

Editors and affiliations

  • S. E. Ahmed
    • 1
  • N. Reid
    • 2
  1. 1.Department of MathematicsUniversity of ReginaReginaCanada
  2. 2.Department of StatisticsUniversity of TorontoTorontoCanada

Bibliographic information

  • DOI https://doi.org/10.1007/978-1-4613-0141-7
  • Copyright Information Springer-Verlag New York, Inc. 2001
  • Publisher Name Springer, New York, NY
  • eBook Packages Springer Book Archive
  • Print ISBN 978-0-387-95018-1
  • Online ISBN 978-1-4613-0141-7
  • Series Print ISSN 0930-0325
  • Buy this book on publisher's site