Reduced Rank Regression

1. Front Matter

   Pages i-x

   PDF

2. Introduction

   • Heinz Schmidli

   Pages 1-4

3. Quantitative Structure Activity Relationships (QSAR)

   • Heinz Schmidli

   Pages 5-15

4. Linear Multivariate Prediction

   • Heinz Schmidli

   Pages 16-37

5. Heuristic Multivariate Prediction Methods

   • Heinz Schmidli

   Pages 38-48

6. Classical Analysis of Reduced Rank Regression

   • Heinz Schmidli

   Pages 49-102

7. Bayesian Analysis of Reduced Rank Regression

   • Heinz Schmidli

   Pages 103-127

8. Case Studies

   • Heinz Schmidli

   Pages 128-151

9. Discussion

   • Heinz Schmidli

   Pages 152-165

10. Back Matter

    Pages 167-179

    PDF

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).

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

• Mathematical Applications, CIBA-GEIGY Ltd., Basel, Switzerland

  Heinz Schmidli

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