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Article: Lack-of-fit Tests For Quantile Regression Models
Title | Lack-of-fit Tests For Quantile Regression Models |
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Authors | |
Keywords | High dimensional data Hypothesis test Lack of fit Quantile regression Two-sample test |
Issue Date | 2019 |
Publisher | Wiley-Blackwell Publishing Ltd. The Journal's web site is located at http://www.blackwellpublishing.com/journals/RSSB |
Citation | Journal of the Royal Statistical Society. Series B: Statistical Methodology, 2019, v. 81 n. 3, p. 629-648 How to Cite? |
Abstract | The paper novelly transforms lack‐of‐fit tests for parametric quantile regression models into checking the equality of two conditional distributions of covariates. Accordingly, by applying some successful two‐sample test statistics in the literature, two tests are constructed to check the lack of fit for low and high dimensional quantile regression models. The low dimensional test works well when the number of covariates is moderate, whereas the high dimensional test can maintain the power when the number of covariates exceeds the sample size. The null distribution of the high dimensional test has an explicit form, and the p‐values or critical values can then be calculated directly. The finite sample performance of the tests proposed is examined by simulation studies, and their usefulness is further illustrated by two real examples. |
Persistent Identifier | http://hdl.handle.net/10722/272964 |
ISSN | 2023 Impact Factor: 3.1 2023 SCImago Journal Rankings: 4.330 |
ISI Accession Number ID |
DC Field | Value | Language |
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dc.contributor.author | Dong, C | - |
dc.contributor.author | Li, G | - |
dc.contributor.author | Feng, X | - |
dc.date.accessioned | 2019-08-06T09:20:00Z | - |
dc.date.available | 2019-08-06T09:20:00Z | - |
dc.date.issued | 2019 | - |
dc.identifier.citation | Journal of the Royal Statistical Society. Series B: Statistical Methodology, 2019, v. 81 n. 3, p. 629-648 | - |
dc.identifier.issn | 1369-7412 | - |
dc.identifier.uri | http://hdl.handle.net/10722/272964 | - |
dc.description.abstract | The paper novelly transforms lack‐of‐fit tests for parametric quantile regression models into checking the equality of two conditional distributions of covariates. Accordingly, by applying some successful two‐sample test statistics in the literature, two tests are constructed to check the lack of fit for low and high dimensional quantile regression models. The low dimensional test works well when the number of covariates is moderate, whereas the high dimensional test can maintain the power when the number of covariates exceeds the sample size. The null distribution of the high dimensional test has an explicit form, and the p‐values or critical values can then be calculated directly. The finite sample performance of the tests proposed is examined by simulation studies, and their usefulness is further illustrated by two real examples. | - |
dc.language | eng | - |
dc.publisher | Wiley-Blackwell Publishing Ltd. The Journal's web site is located at http://www.blackwellpublishing.com/journals/RSSB | - |
dc.relation.ispartof | Journal of the Royal Statistical Society. Series B: Statistical Methodology | - |
dc.rights | This is the peer reviewed version of the following article: Journal of the Royal Statistical Society. Series B: Statistical Methodology, 2019, v. 81 n. 3, p. 629-648, which has been published in final form at https://doi.org/10.1111/rssb.12321. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Use of Self-Archived Versions. | - |
dc.subject | High dimensional data | - |
dc.subject | Hypothesis test | - |
dc.subject | Lack of fit | - |
dc.subject | Quantile regression | - |
dc.subject | Two-sample test | - |
dc.title | Lack-of-fit Tests For Quantile Regression Models | - |
dc.type | Article | - |
dc.identifier.email | Li, G: gdli@hku.hk | - |
dc.identifier.authority | Li, G=rp00738 | - |
dc.description.nature | postprint | - |
dc.identifier.doi | 10.1111/rssb.12321 | - |
dc.identifier.scopus | eid_2-s2.0-85065185568 | - |
dc.identifier.hkuros | 299669 | - |
dc.identifier.volume | 81 | - |
dc.identifier.issue | 3 | - |
dc.identifier.spage | 629 | - |
dc.identifier.epage | 648 | - |
dc.identifier.isi | WOS:000470714200007 | - |
dc.publisher.place | United Kingdom | - |
dc.identifier.issnl | 1369-7412 | - |