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Activity Number: 593 - Computationally Intensive and Machine Learning Methods
Type: Contributed
Date/Time: Wednesday, August 1, 2018 : 2:00 PM to 3:50 PM
Sponsor: Section on Statistical Computing
Abstract #330679
Title: New Computational Methods for Non/Semiparametric Quantile Regression Models
Author(s): Bo Kai* and Mian Huang and Weixin Yao and Yuexiao Dong
Companies: College of Charleston and Shanghai University of Finance and Economics and University of California, Riverside and Temple University
Keywords: quantile regression; local approximation; nonparametric regression; semiparametric regression

Quantile regression aims at estimating the conditional quantiles of the response variable. Compared to least squares regression, quantile regression provides a more comprehensive picture of the relationship between the response and its covariates. The optimization for quantile regression is challenging because the objective function is non-differentiable. In this work, we focus on the optimization problems in nonparametric quantile regression and its related models. Existing algorithms may yield estimates that are not very smooth or stable. To address these issues, we propose a new class of algorithms which produce smoother and stabler estimates in nonparametric quantile regression models. The finite sample performance of the proposed algorithms is investigated in several numerical studies.

Authors who are presenting talks have a * after their name.

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