Sufficient dimension reduction is a rapidly develo** research field that has wide
applications in regression diagnostics, data visualization, machine learning, genomics …

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

Sufficient dimension reduction is an important branch of dimension reduction, which
includes variable selection and projection methods. Most of the sufficient dimension …

Neural networks are combined with sufficient dimension reduction methodology in order to
remove the limitation of small p and n of the latter. NN-SDR applies when the dependence of …

Recent efforts to characterize the human microbiome and its relation to chronic diseases
have led to a surge in statistical development for compositional data. We develop likelihood …

The present paper introduces a novel method for the construction of Socioeconomic Status
(SES) indices that are specific to a target variable of interest. It is based on the Sufficient …

Most data sets comprise of measurements on continuous and categorical variables. Yet,
modeling high-dimensional mixed predictors has received limited attention in the regression …

We propose and study structured time‐dependent inverse regression (STIR), a novel
sufficient dimension reduction model, to analyze longitudinally measured, correlated …

Graphical models allow modeling of complex dependencies among components of a
random vector. In many applications of graphical models, however, for example microbiome …

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 …