Model averaging marginal regression for high dimensional conditional quantile prediction

Jingwen Tu, Hu Yang, Chaohui Guo () and Jing Lv
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Jingwen Tu: Chongqing University
Hu Yang: Chongqing University
Chaohui Guo: Chongqing University
Jing Lv: Southwest University

Statistical Papers, 2021, vol. 62, issue 6, No 7, 2689 pages

Abstract: Abstract In this article, we propose a high dimensional semiparametric model average approach to predict the conditional quantile of the response variable. Firstly, we approximate the multivariate conditional quantile function by an affine combination of one-dimensional marginal conditional quantile functions which can be estimated by the local linear regression. Secondly, based on the estimated marginal quantile regression functions, a penalized quantile regression is proposed to estimate and select the significant model weights involved in the approximation. Under some mild conditions, we have established the asymptotic properties for both the parametric and nonparametric estimators. Finally, we evaluate the finite sample performance of the proposed procedure via simulations and a real data analysis.

Keywords: Kernel estimation; Marginal regression; Model averaging; Penalized quantile regression; Prediction accuracy (search for similar items in EconPapers)
Date: 2021
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DOI: 10.1007/s00362-020-01212-1

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