Reduced Rank Regression e-bog
875,33 DKK
(inkl. moms 1094,16 DKK)
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. ...
E-bog
875,33 DKK
Forlag
Physica
Udgivet
13 marts 2013
Genrer
Economics, Finance, Business and Management
Sprog
English
Format
pdf
Beskyttelse
LCP
ISBN
9783642500152
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).