On expectile-assisted inverse regression estimation for sufficient dimension reduction

2019/10/24 by Abdul‐Nasah Soale, Soale, Abdul-Nasah, Yuexiao Dong +1
Computer Science · Mathematics · #Bayesian Methods and Mixture Models #Computation (stat.CO) #FOS: Computer and information sciences #Gaussian Processes and Bayesian Inference #Methodology (stat.ME) #Statistical Methods and Inference

paper · pdf · doi:10.48550/arxiv.1910.10898

openalex publication_date 2019/10/24 · openalex created_date 2019/11/01 · openalex updated_date 2026/07/28

Abstract

Moment-based sufficient dimension reduction methods such as sliced inverse regression may not work well in the presence of heteroscedasticity. We propose to first estimate the expectiles through kernel expectile regression, and then carry out dimension reduction based on random projections of the regression expectiles. Several popular inverse regression methods in the literature are extended under this general framework. The proposed expectile-assisted methods outperform existing moment-based dimension reduction methods in both numerical studies and an analysis of the Big Mac data.

Citations

• Successive direction extraction for estimating the central subspace in a multiple-index regression

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