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).
Author(s): Dr. Heinz Schmidli (auth.)
Series: Contributions to Statistics
Edition: 1
Publisher: Physica-Verlag Heidelberg
Year: 1995
Language: English
Pages: 179
Tags: Math. Applications in Chemistry; Economic Theory; Statistics for Business/Economics/Mathematical Finance/Insurance; Theoretical and Computational Chemistry
Front Matter....Pages i-x
Introduction....Pages 1-4
Quantitative Structure Activity Relationships (QSAR)....Pages 5-15
Linear Multivariate Prediction....Pages 16-37
Heuristic Multivariate Prediction Methods....Pages 38-48
Classical Analysis of Reduced Rank Regression....Pages 49-102
Bayesian Analysis of Reduced Rank Regression....Pages 103-127
Case Studies....Pages 128-151
Discussion....Pages 152-165
Back Matter....Pages 167-179
