Electronic Journal of Statistics

Principal quantile regression for sufficient dimension reduction with heteroscedasticity

Chong Wang, Seung Jun Shin, and Yichao Wu

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Sufficient dimension reduction (SDR) is a successful tool for reducing data dimensionality without stringent model assumptions. In practice, data often display heteroscedasticity which is of scientific importance in general but frequently overlooked since a primal goal of most existing statistical methods is to identify conditional mean relationship among variables. In this article, we propose a new SDR method called principal quantile regression (PQR) that efficiently tackles heteroscedasticity. PQR can naturally be extended to a nonlinear version via kernel trick. Asymptotic properties are established and an efficient solution path-based algorithm is provided. Numerical examples based on both simulated and real data demonstrate the PQR’s advantageous performance over existing SDR methods. PQR still performs very competitively even for the case without heteroscedasticity.

Article information

Electron. J. Statist., Volume 12, Number 2 (2018), 2114-2140.

Received: March 2017
First available in Project Euclid: 13 July 2018

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Primary: 60K35: Interacting random processes; statistical mechanics type models; percolation theory [See also 82B43, 82C43]

Heteroscedasticity kernel quantile regression principal quantile regression sufficient dimension reduction

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Wang, Chong; Shin, Seung Jun; Wu, Yichao. Principal quantile regression for sufficient dimension reduction with heteroscedasticity. Electron. J. Statist. 12 (2018), no. 2, 2114--2140. doi:10.1214/18-EJS1432. https://projecteuclid.org/euclid.ejs/1531468823

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