Analysis of high-dimensional data is becoming the norm in a variety of scientific studies and dimension reduction methods are widely employed. As the predictor domain knowledge is often available, it is useful to incorporate such domain information into dimension reduction and subsequent model...

The importance of variable selection in regression has grown in recent years as computing power has encouraged the modelling of data sets of ever-increasing size. Data mining applications in finance, marketing and bioinformatics are obvious examples. A limitation of nearly all existing variable...

There have been an increasing number of applications where the number of predictors is large, meanwhile data are repeatedly measured at a sequence of time points. In this article we investigate how dimension reduction method can be employed for analyzing such high-dimensional longitudinal data....

In regression with a vector of quantitative predictors, sufficient dimension reduction methods can effectively reduce the predictor dimension, while preserving full regression information and assuming no parametric model. However, all current reduction methods require the sample size n to be...

Existing sufficient dimension reduction methods suffer from the fact that each dimension reduction component is a linear combination of all the original predictors, so that it is difficult to interpret the resulting estimates. We propose a unified estimation strategy, which combines a...