Introduction to Optimization Methods and their Application in Statistics

  • B. S. Everitt

Table of contents

  1. Front Matter
    Pages i-vii
  2. B. S. Everitt
    Pages 1-10
  3. B. S. Everitt
    Pages 11-20
  4. B. S. Everitt
    Pages 21-27
  5. B. S. Everitt
    Pages 42-58
  6. B. S. Everitt
    Pages 59-79
  7. Back Matter
    Pages 80-88

About this book

Introduction

Optimization techniques are used to find the values of a set of parameters which maximize or minimize some objective function of interest. Such methods have become of great importance in statistics for estimation, model fitting, etc. This text attempts to give a brief introduction to optimization methods and their use in several important areas of statistics. It does not pretend to provide either a complete treatment of optimization techniques or a comprehensive review of their application in statistics; such a review would, of course, require a volume several orders of magnitude larger than this since almost every issue of every statistics journal contains one or other paper which involves the application of an optimization method. It is hoped that the text will be useful to students on applied statistics courses and to researchers needing to use optimization techniques in a statistical context. Lastly, my thanks are due to Bertha Lakey for typing the manuscript.

Keywords

applied statistics cluster analysis factor analysis fitting generalized linear model likelihood linear regression mathematical programming multidimensional scaling optimization statistics

Authors and affiliations

  • B. S. Everitt
    • 1
  1. 1.Institute of PsychiatryEngland

Bibliographic information

  • DOI https://doi.org/10.1007/978-94-009-3153-4
  • Copyright Information Springer Science+Business Media B.V. 1987
  • Publisher Name Springer, Dordrecht
  • eBook Packages Springer Book Archive
  • Print ISBN 978-94-010-7917-4
  • Online ISBN 978-94-009-3153-4
  • About this book