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Article: Estimation in quantile regression models for correlated data with diverging number of covariates and large cluster sizes
Title | Estimation in quantile regression models for correlated data with diverging number of covariates and large cluster sizes |
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Authors | |
Keywords | Clustered data Diverging dimensionality Induced smoothing Quadratic inference functions Quantile regressionI |
Issue Date | 2021 |
Publisher | Taylor & Francis Inc. The Journal's web site is located at http://www.tandf.co.uk/journals/titles/03610918.asp |
Citation | Communications in Statistics: Simulation and Computation, 2021 How to Cite? |
Abstract | In many data analytic problems, repeated measurements with a large number of covariates are collected and conditional quantile modeling for such correlated data are often of significant interest, especially in medical applications. We propose a quadratic inference functions based approach to take into account the correlations within clusters and use smoothing to make the objective function amenable to computation. We show that the asymptotic properties of the estimators are the same whether or not smoothing is applied, established in the “diverging p, large n” setting. The cluster sizes are also allowed to diverge with sample size n. Simulation results are presented to demonstrate the effectiveness of the proposed estimator by taking into account the within-cluster correlations and we use a longitudinal data set to illustrate the method. |
Description | The research of Rui Li was supported by National Social Science Fund of China (No. 17BTJ025). |
Persistent Identifier | http://hdl.handle.net/10722/306529 |
ISSN | 2023 Impact Factor: 0.8 2023 SCImago Journal Rankings: 0.440 |
ISI Accession Number ID |
DC Field | Value | Language |
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dc.contributor.author | Zhao, W | - |
dc.contributor.author | ZHANG, X | - |
dc.contributor.author | Yuen, KC | - |
dc.contributor.author | Li, R | - |
dc.contributor.author | Lian, H | - |
dc.date.accessioned | 2021-10-22T07:35:55Z | - |
dc.date.available | 2021-10-22T07:35:55Z | - |
dc.date.issued | 2021 | - |
dc.identifier.citation | Communications in Statistics: Simulation and Computation, 2021 | - |
dc.identifier.issn | 0361-0918 | - |
dc.identifier.uri | http://hdl.handle.net/10722/306529 | - |
dc.description | The research of Rui Li was supported by National Social Science Fund of China (No. 17BTJ025). | - |
dc.description.abstract | In many data analytic problems, repeated measurements with a large number of covariates are collected and conditional quantile modeling for such correlated data are often of significant interest, especially in medical applications. We propose a quadratic inference functions based approach to take into account the correlations within clusters and use smoothing to make the objective function amenable to computation. We show that the asymptotic properties of the estimators are the same whether or not smoothing is applied, established in the “diverging p, large n” setting. The cluster sizes are also allowed to diverge with sample size n. Simulation results are presented to demonstrate the effectiveness of the proposed estimator by taking into account the within-cluster correlations and we use a longitudinal data set to illustrate the method. | - |
dc.language | eng | - |
dc.publisher | Taylor & Francis Inc. The Journal's web site is located at http://www.tandf.co.uk/journals/titles/03610918.asp | - |
dc.relation.ispartof | Communications in Statistics: Simulation and Computation | - |
dc.rights | This is an Accepted Manuscript of an article published by Taylor & Francis in [JOURNAL TITLE] on [date of publication], available online: http://www.tandfonline.com/[Article DOI]. | - |
dc.subject | Clustered data | - |
dc.subject | Diverging dimensionality | - |
dc.subject | Induced smoothing | - |
dc.subject | Quadratic inference functions | - |
dc.subject | Quantile regressionI | - |
dc.title | Estimation in quantile regression models for correlated data with diverging number of covariates and large cluster sizes | - |
dc.type | Article | - |
dc.identifier.email | Yuen, KC: kcyuen@hku.hk | - |
dc.identifier.authority | Yuen, KC=rp00836 | - |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.1080/03610926.2021.1922701 | - |
dc.identifier.scopus | eid_2-s2.0-85107555897 | - |
dc.identifier.hkuros | 328393 | - |
dc.identifier.isi | WOS:000658965300001 | - |
dc.publisher.place | United States | - |