A Gaussian pseudolikelihood approach for quantile regression with repeated measurements

Fu, Liya, Wang, You-Gan and Zhu, Min (2015) A Gaussian pseudolikelihood approach for quantile regression with repeated measurements. Computational Statistics and Data Analysis, 84 41-53. doi:10.1016/j.csda.2014.11.002


Author Fu, Liya
Wang, You-Gan
Zhu, Min
Title A Gaussian pseudolikelihood approach for quantile regression with repeated measurements
Journal name Computational Statistics and Data Analysis   Check publisher's open access policy
ISSN 0167-9473
1872-7352
Publication date 2015-04-01
Year available 2014
Sub-type Article (original research)
DOI 10.1016/j.csda.2014.11.002
Volume 84
Start page 41
End page 53
Total pages 13
Place of publication Amsterdam, Netherlands
Publisher Elsevier
Collection year 2015
Language eng
Abstract 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.
Keyword Gaussian estimation
Induced smoothing method
Pseudolikelihood
Repeated measurements
Working covariance matrix
Q-Index Code C1
Q-Index Status Confirmed Code
Institutional Status UQ
Additional Notes Published online ahead of print 13 Nov 2014

Document type: Journal Article
Sub-type: Article (original research)
Collections: School of Mathematics and Physics
Official 2015 Collection
 
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