Written by the leading expert in the field, this text reviews the major new developments in
envelope models and methods An Introduction to Envelopes provides an overview of the …

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We describe the current and potential future roles for partial least squares (PLS) algorithms
in path analyses, guided by recent advances in envelope theory. After reviewing the present …

We review probabilistic principal components, principal fitted components, sufficient
dimension reduction, and envelopes, arguing that at their core they are all based on …

Statistics is the science of extracting useful information from data, and a statistical model is
used to provide a useful approximation to some of the important characteristics of the …

The envelope model allows efficient estimation in multivariate linear regression. In this
paper, we propose the sparse envelope model, which is motivated by applications where …

A supervised singular value decomposition (SupSVD) model has been developed for
supervised dimension reduction where the low rank structure of the data of interest is …

A Bayesian approach for envelope models Page 1 The Annals of Statistics 2017, Vol. 45, No.
1, 196–222 DOI: 10.1214/16-AOS1449 © Institute of Mathematical Statistics, 2017 A …

The quantile regression method is a valuable complement to the classical mean regression,
helping to ensure robust and comprehensive data analyses in a variety of applications. We …

Dimension reduction provides a useful tool for analyzing high-dimensional data. The
recently developed envelope method is a parsimonious version of the classical multivariate …