Relationships between sets of variables of different kinds are of interest in many branches of
science. The question of the analysis of relationships of this sort has nevertheless rather …

Not so long ago, multivariate analysis consisted solely of linear methods illustrated on small
to medium-sized data sets. Moreover, statistical computing meant primarily batch processing …

This paper compares several methods (ordinary least squares, nonlinear least squares,
maximum likelihood in an error correction model, principal components, and canonical …

Regression methods are perhaps the most widely used statistical tools in data analysis.
When several response variables are studied simultaneously, we are in the sphere of …

This article introduces a class of statistical tests for the hypothesis that some feature that is
present in each of several variables is common to them. Features are data properties such …

This paper is concerned with the investigation of reduced rank coefficient models for multiple
time series. In particular, autoregressive processes which have a structure to their coefficient …

This is the companion second volume to my undergraduate text Fundamentals of
Probability: A First Course. The purpose of my writing this book is to give graduate students …

We consider Bayesian inference about the dimensionality in the multivariate reduced rank
regression framework, which encompasses several models such as MANOVA, factor …

Interest has been growing in the use and extensions of multivariate reduced rank regression
procedures in applied research and data modelling. This paper considers an extension of …

Abstract Principal Component Analysis with respect to Instrumental Variables (PCAIV) is a
statistical tool for exploratory analysis combining both principal component analysis and …