Advertisement

Linear Models

Least Squares and Alternatives

  • Calyampudi Radhakrishna Rao
  • Helge Toutenburg

Part of the Springer Series in Statistics book series (SSS)

Table of contents

  1. Front Matter
    Pages i-xi
  2. Calyampudi Radhakrishna Rao, Helge Toutenburg
    Pages 1-2
  3. Calyampudi Radhakrishna Rao, Helge Toutenburg
    Pages 3-18
  4. Calyampudi Radhakrishna Rao, Helge Toutenburg
    Pages 19-87
  5. Calyampudi Radhakrishna Rao, Helge Toutenburg
    Pages 89-110
  6. Calyampudi Radhakrishna Rao, Helge Toutenburg
    Pages 111-154
  7. Calyampudi Radhakrishna Rao, Helge Toutenburg
    Pages 155-176
  8. Calyampudi Radhakrishna Rao, Helge Toutenburg
    Pages 177-202
  9. Calyampudi Radhakrishna Rao, Helge Toutenburg
    Pages 203-227
  10. Calyampudi Radhakrishna Rao, Helge Toutenburg
    Pages 229-245
  11. Calyampudi Radhakrishna Rao, Helge Toutenburg
    Pages 247-284
  12. Back Matter
    Pages 285-353

About this book

Introduction

The book is based on both authors' several years of experience in teaching linear models at various levels. It gives an up-to-date account of the theory and applications of linear models. The book can be used as a text for courses in statistics at the graduate level and as an accompanying text for courses in other areas. Some of the highlights in this book are as follows. A relatively extensive chapter on matrix theory (Appendix A) provides the necessary tools for proving theorems discussed in the text and offers a selection of classical and modern algebraic results that are useful in research work in econometrics, engineering, and optimization theory. The matrix theory of the last ten years has produced a series of fundamental results about the definiteness of matrices, especially for the differences of matrices, which enable superiority comparisons of two biased estimates to be made for the first time. We have attempted to provide a unified theory of inference from linear models with minimal assumptions. Besides the usual least-squares theory, alternative methods of estimation and testing based on convex loss func­ tions and general estimating equations are discussed. Special emphasis is given to sensitivity analysis and model selection. A special chapter is devoted to the analysis of categorical data based on logit, loglinear, and logistic regression models. The material covered, theoretical discussion, and its practical applica­ tions will be useful not only to students but also to researchers and con­ sultants in statistics.

Keywords

Linear Models Logit Matrix Rang Scala algebra equation form linear regression matrices minimum model selection statistics testing

Authors and affiliations

  • Calyampudi Radhakrishna Rao
    • 1
  • Helge Toutenburg
    • 2
  1. 1.Department of StatisticsThe Pennsylvania State UniversityUniversity ParkUSA
  2. 2.Institut für StatistikUniversität MünchenMünchenGermany

Bibliographic information

  • DOI https://doi.org/10.1007/978-1-4899-0024-1
  • Copyright Information Springer-Verlag New York 1995
  • Publisher Name Springer, New York, NY
  • eBook Packages Springer Book Archive
  • Print ISBN 978-1-4899-0026-5
  • Online ISBN 978-1-4899-0024-1
  • Series Print ISSN 0172-7397
  • Buy this book on publisher's site
Industry Sectors
Pharma
Biotechnology
Finance, Business & Banking
IT & Software
Telecommunications
Consumer Packaged Goods
Aerospace
Oil, Gas & Geosciences
Engineering