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Bayesian Reduced Rank Regression for Classification

  • Heinz SchmidliEmail author
Chapter
Part of the Studies in Classification, Data Analysis, and Knowledge Organization book series (STUDIES CLASS)

Abstract

Many classical multivariate analysis methods are special cases of reduced rank regression, including canonical correlation analysis, redundancy analysis, and R. A. Fisher’s linear discriminant analysis. The latter classifies an object based on a few linear combinations of its multivariate measurements. Classical inference for linear discriminant analysis may be based on asymptotic theory or resampling methods. A Bayesian linear discriminant analysis is proposed using Bayesian reduced rank regression as a starting point. The model can be implemented in Bayesian software with Markov chain Monte Carlo approaches and is easily extendable.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Novartis Statistical MethodologyBaselSwitzerland

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