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Activity Number: 176 - Modeling
Type: Contributed
Date/Time: Monday, July 30, 2018 : 10:30 AM to 12:20 PM
Sponsor: Section on Statistical Computing
Abstract #328848
Title: On Nonparametric Quantile Regression
Author(s): Mei Ling Huang and Jenny Tieu*
Companies: Brock University and Brock University
Keywords: Conditional quantile; extreme value distribution; Gumbel.s second kind of bivariate exponential distribution; generalized Pareto distribution; nonparametric regression; loss function

Quantile regression estimates conditional quantiles and has wide applications in the real world. Estimating high conditional quantiles is an important problem. The regular quantile regression (QR) method often sets a linear or non-linear model, then estimates the coefficients to obtain the estimated conditional quantile. This approach may be restricted by the model setting. To overcome this problem, this paper proposes a direct nonparametric quantile regression method. The asymptotic properties of this direct estimator are given. Monte Carlo simulations show good efficiency for the proposed direct nonparametric QR estimator relative to the regular QR estimator. The paper also investigates a real-world example of applications by using the proposed method. Comparisons of the proposed method and existing methods are given.

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

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