Purpose: This study sought to review the characteristics, strengths, weaknesses variants,
applications areas and data types applied on the various Dimension Reduction techniques …

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 …

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

The R 2 statistic and its classic adjusted version, say R* 2, tend to overestimate the multiple
correlation coefficient when dealing with multivariate data that exhibit heavy tails and tail …

Vector autoregressive (VAR) models have become a popular choice for modeling
multivariate time series data due to their simplicity and ease of use. Efficient estimation of …

Partial least squares (PLS) regression is, at its historical core, a black-box algorithmic
method for dimension reduction and prediction based on an underlying linear relationship …

This paper introduces a novel supervised dimension reduction method for classification and
regression problems using reproducing kernel Hilbert spaces. The proposed approach …

Efficient estimation in expectile regression using envelope models Page 1 Electronic Journal of
Statistics Vol. 14 (2020) 143–173 ISSN: 1935-7524 https://doi.org/10.1214/19-EJS1664 Efficient …

Due to the increasing development of information technologies and their applications in
many scientific fields, high-dimensional time series data are routinely collected across a …