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

In this paper, we review three families of methods in linear sufficient dimension reduction
through optimization. Through minimization of general loss functions, we cast classical …

As relevant technologies advance, streamed data are frequently encountered in various
applications, and the need for scalable algorithms becomes urgent. In this article, we …

We propose a real-time approach for sufficient dimension reduction. Compared with popular
sufficient dimension reduction methods including sliced inverse regression and principal …

The proof of L 2 consistency for the k th nearest neighbour distance estimator of the
Shannon entropy for an arbitrary fixed k≥ 1 is provided. It is constructed the non-parametric …

In this paper, the grain loss assessment was studied based on logistic regression, and 5400
samples of 31 provinces in our country in the year 2012-2014 were selected, and the 7 …

Quantile regression (QR) is becoming increasingly popular due to its relevance in many
scientific investigations. There is a great amount of work about linear and nonlinear QR …

Sufficient dimension reduction (SDR) is a popular supervised machine learning technique
that reduces the predictor dimension and facilitates subsequent data analysis in practice. In …

Principal asymmetric least squares (PALS) is introduced as a novel method for sufficient
dimension reduction with heteroscedastic error. Classical methods such as MAVE [Xia et …

In this paper, we introduce a sufficient dimension reduction (SDR) algorithm based on
distance‐weighted discrimination (DWD). Our methods is shown to be robust on the …