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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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 |
work_keys_str_mv |
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