A nonparametric approach for quantile regression

Abstract 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 designs a linear or non-linear model, then estimates the coefficients to obtain t...

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Main Authors: Mei Ling Huang, Christine Nguyen
Format: Article
Language:English
Published: SpringerOpen 2018-07-01
Series:Journal of Statistical Distributions and Applications
Subjects:
Online Access:http://link.springer.com/article/10.1186/s40488-018-0084-9
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spelling doaj-dc2ea36fd8eb4ddf9056087b3ae0f8792020-11-25T00:32:38ZengSpringerOpenJournal of Statistical Distributions and Applications2195-58322018-07-015111410.1186/s40488-018-0084-9A nonparametric approach for quantile regressionMei Ling Huang0Christine Nguyen1Department of Mathematics & Statistics, Brock UniversityApotex Inc.Abstract 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 designs a linear or non-linear model, then estimates the coefficients to obtain the estimated conditional quantiles. This approach may be restricted by the linear model setting. To overcome this problem, this paper proposes a direct nonparametric quantile regression method with five-step algorithm. Monte Carlo simulations show good efficiency for the proposed direct QR estimator relative to the regular QR estimator. The paper also investigates two real-world examples of applications by using the proposed method. Studies of the simulations and the examples illustrate that the proposed direct nonparametric quantile regression model fits the data set better than the regular quantile regression method.http://link.springer.com/article/10.1186/s40488-018-0084-9Conditional quantileGoodness-of-fitGumbel’s second kind of bivariate exponential distributionNonparametric kernel density estimatorNonparametric regressionWeighted loss function
collection DOAJ
language English
format Article
sources DOAJ
author Mei Ling Huang
Christine Nguyen
spellingShingle Mei Ling Huang
Christine Nguyen
A nonparametric approach for quantile regression
Journal of Statistical Distributions and Applications
Conditional quantile
Goodness-of-fit
Gumbel’s second kind of bivariate exponential distribution
Nonparametric kernel density estimator
Nonparametric regression
Weighted loss function
author_facet Mei Ling Huang
Christine Nguyen
author_sort Mei Ling Huang
title A nonparametric approach for quantile regression
title_short A nonparametric approach for quantile regression
title_full A nonparametric approach for quantile regression
title_fullStr A nonparametric approach for quantile regression
title_full_unstemmed A nonparametric approach for quantile regression
title_sort nonparametric approach for quantile regression
publisher SpringerOpen
series Journal of Statistical Distributions and Applications
issn 2195-5832
publishDate 2018-07-01
description Abstract 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 designs a linear or non-linear model, then estimates the coefficients to obtain the estimated conditional quantiles. This approach may be restricted by the linear model setting. To overcome this problem, this paper proposes a direct nonparametric quantile regression method with five-step algorithm. Monte Carlo simulations show good efficiency for the proposed direct QR estimator relative to the regular QR estimator. The paper also investigates two real-world examples of applications by using the proposed method. Studies of the simulations and the examples illustrate that the proposed direct nonparametric quantile regression model fits the data set better than the regular quantile regression method.
topic Conditional quantile
Goodness-of-fit
Gumbel’s second kind of bivariate exponential distribution
Nonparametric kernel density estimator
Nonparametric regression
Weighted loss function
url http://link.springer.com/article/10.1186/s40488-018-0084-9
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