 Projection expectile regression for sufficient dimension reductionAbdul-Nasah Soale Computational Statistics & Data Analysis, 2023, vol. 180, issue C Abstract: Many existing sufficient dimension reduction methods are designed for regression with predictors that are elliptically distributed, which limits their application in real data analyses. Projection expectile regression (PER) is proposed as a new linear sufficient dimension reduction method for handling complex predictor structures, which includes continuous, discrete, and mixed predictor variables. PER requires the link function between the response and the predictor to be monotone, but not necessarily smooth, which makes it suitable for handling stratified response surfaces. By design, PER does not involve matrix inversion or high-dimensional smoothing. Thus, PER is ideal for controlling problems associated with multicollinearity, high dimensionality, and sparsity in the predictor. An extensive simulation study demonstrates the performance of projection expectile regression in synthetic data. A real data analysis of health insurance charges in the United States is also provided. The asymptotic properties of the PER estimator are included as well. Keywords: Expectile regression; Complex predictor structure; Dimension reduction; Linearity assumption; Discrete predictors (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:180:y:2023:i:c:s0167947322002468 DOI: 10.1016/j.csda.2022.107666 Access Statistics for this article Computational Statistics & Data Analysis is currently edited by S.P. Azen More articles in Computational Statistics & Data Analysis from Elsevier |
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