Fitting Linear Models

An Application of Conjugate Gradient Algorithms

  • Allen Mclntosh

Part of the Lecture Notes in Statistics book series (LNS, volume 10)

Table of contents

  1. Front Matter
    Pages i-vi
  2. Allen Mclntosh
    Pages 1-7
  3. Allen Mclntosh
    Pages 8-25
  4. Allen Mclntosh
    Pages 26-40
  5. Allen Mclntosh
    Pages 41-67
  6. Allen Mclntosh
    Pages 68-101
  7. Allen Mclntosh
    Pages 102-115
  8. Allen Mclntosh
    Pages 116-124
  9. Allen Mclntosh
    Pages 125-126
  10. Back Matter
    Pages 127-201

About this book

Introduction

The increasing power and decreasing price of smalI computers, especialIy "personal" computers, has made them increasingly popular in statistical analysis. The day may not be too far off when every statistician has on his or her desktop computing power on a par with the large mainframe computers of 15 or 20 years ago. These same factors make it relatively easy to acquire and manipulate large quantities of data, and statisticians can expect a corresponding increase in the size of the datasets that they must analyze. Unfortunately, because of constraints imposed by architecture, size or price, these smalI computers do not possess the main memory of their large cousins. Thus, there is a growing need for algorithms that are sufficiently economical of space to permit statistical analysis on smalI computers. One area of analysis where there is a need for algorithms that are economical of space is in the fitting of linear models.

Keywords

Fitting Generalized linear model Lineares Modell Methode der konjugierten Gradienten Statistische Versuchsplanung algorithms best fit

Authors and affiliations

  • Allen Mclntosh
    • 1
  1. 1.Bell Telephone Laboratories, Inc.New JerseyUSA

Bibliographic information

  • DOI https://doi.org/10.1007/978-1-4612-5752-3
  • Copyright Information Springer-Verlag New York 1982
  • Publisher Name Springer, New York, NY
  • eBook Packages Springer Book Archive
  • Print ISBN 978-0-387-90746-8
  • Online ISBN 978-1-4612-5752-3
  • Series Print ISSN 0930-0325
  • About this book