Title: Reduced rank regression
First published: 1995
Description: 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).
Subjects: Bayesian statistical decision theory, QSAR (Biochemistry), Multivariate analysis, Regression analysis, Economics, Chemistry, Mathematics, Statistics, Math. Applications in Chemistry, Statistics for Business/Economics/Mathematical Finance/Insurance, Theoretical and Computational Chemistry
LC classification: QA278.2 .S3654 1995