A Gaussian pseudolikelihood approach for quantile regression with repeated measurements

Fu, Liya, Wang, You-Gan, & Zhu, Min (2015) A Gaussian pseudolikelihood approach for quantile regression with repeated measurements. Computational Statistics and Data Analysis, 84, pp. 41-53.

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To enhance the efficiency of regression parameter estimation by modeling the correlation structure of correlated binary error terms in quantile regression with repeated measurements, we propose a Gaussian pseudolikelihood approach for estimating correlation parameters and selecting the most appropriate working correlation matrix simultaneously. The induced smoothing method is applied to estimate the covariance of the regression parameter estimates, which can bypass density estimation of the errors. Extensive numerical studies indicate that the proposed method performs well in selecting an accurate correlation structure and improving regression parameter estimation efficiency. The proposed method is further illustrated by analyzing a dental dataset.

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ID Code: 79331
Item Type: Journal Article
Refereed: Yes
Keywords: Gaussian estimation, Induced smoothing method, Pseudolikelihood, Repeated measurements, Working covariance matrix
DOI: 10.1016/j.csda.2014.11.002
ISSN: 0167-9473
Divisions: Current > QUT Faculties and Divisions > QUT Business School
Current > Schools > School of Economics & Finance
Copyright Owner: Copyright 2014 Elsevier B.V.
Copyright Statement: NOTICE: this is the author’s version of a work that was accepted for publication in Computational Statistics and Data Analysis. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Computational Statistics and Data Analysis, Volume 84, April 2015, DOI: 10.1016/j.csda.2014.11.002
Deposited On: 09 Dec 2014 22:50
Last Modified: 12 Dec 2015 06:59

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