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 …

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 …

The envelope model was first introduced as a parsimonious version of multivariate linear
regression. It uses dimension reduction techniques to remove immaterial variation in the …

The standard vector autoregressive (VAR) models suffer from overparameterization which is
a serious issue for high-dimensional time series data as it restricts the number of variables …

The envelope model aims to increase efficiency in multivariate analysis by using dimension
reduction techniques. It has been used in many contexts including linear regression …

We describe and elaborate on foundations that connect partial least squares regression with
recently developed envelope theory and methodology. These foundations explain why PLS …

We describe how many dimension reduction strategies are connected conceptually and
philosophically, paving the way for a unified approach to multivariate dimension reduction in …

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 …

Partial least squares (PLS) regression is a popular alternative to ordinary least squares
regression because of its superior prediction performance demonstrated in many cases. In …

Huber regression (HR) is a popular flexible alternative to the least squares regression when
the error follows a heavy-tailed distribution. We propose a new method called the enveloped …