Year: 2024
Type: article
Abstract: Sufficient dimension reduction (SDR) is a main stream of the dimension reduction technique in modern statistical research. It aims at replacing the original predictor X in the data with some reduced predictor β^TX without loss of information for modeling the response Y. In this paper, we briefly review the literature of SDR, including its basic theory and major types of methods such as the inverse regression methods, the minimum average variance estimation, and the semi-parametric methods based on estimating equations. We also review some recent advances including the sparse and non-sparse SDR methods under high-dimensional settings, the application of SDR in causal inference, and nonlinear SDR.
Source: Scientia Sinica Mathematica
Author: Luo Wei
Language: English
Subfield: Statistics and Probability
Field: Mathematics
Domain: Physical Sciences
Open Access status: bronze
OpenAlex ID: W4404289372