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Reduced Rank Regression

With Applications to Quantitative Structure-Activity Relationships

  • Heinz¬†Schmidli
Book

Part of the Contributions to Statistics book series (CONTRIB.STAT.)

Table of contents

  1. Front Matter
    Pages i-x
  2. Heinz Schmidli
    Pages 1-4
  3. Heinz Schmidli
    Pages 16-37
  4. Heinz Schmidli
    Pages 38-48
  5. Heinz Schmidli
    Pages 49-102
  6. Heinz Schmidli
    Pages 103-127
  7. Heinz Schmidli
    Pages 128-151
  8. Heinz Schmidli
    Pages 152-165
  9. Back Matter
    Pages 167-179

About this book

Introduction

Reduced rank regression is widely used in statistics to model multivariate data. In this monograph, theoretical and data analytical approaches are developed for the application of reduced rank regression in multivariate prediction problems. For the first time, both classical and Bayesian inference is discussed, using recently proposed procedures such as the ECM-algorithm and the Gibbs sampler. All methods are motivated and illustrated by examples taken from the area of quantitative structure-activity relationships (QSAR).

Keywords

Covariance matrix Latent variable model Likelihood Multivariate Verfahren Regression Analysis Regressionsanalyse Regressionsmodelle Variance correlation multivariate analysis principal component analysis regression models

Authors and affiliations

  • Heinz¬†Schmidli
    • 1
  1. 1.Mathematical ApplicationsCIBA-GEIGY Ltd.BaselSwitzerland

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-642-50015-2
  • Copyright Information Physica-Verlag Heidelberg 1995
  • Publisher Name Physica-Verlag HD
  • eBook Packages Springer Book Archive
  • Print ISBN 978-3-7908-0871-1
  • Online ISBN 978-3-642-50015-2
  • Series Print ISSN 1431-1968
  • Buy this book on publisher's site