Efficient parameter estimation via Gaussian copulas for quantile regression with longitudinal data

Fu, Liya and Wang, You-Gan (2016) Efficient parameter estimation via Gaussian copulas for quantile regression with longitudinal data. Journal of Multivariate Analysis, 143 492-502. doi:10.1016/j.jmva.2015.07.004


Author Fu, Liya
Wang, You-Gan
Title Efficient parameter estimation via Gaussian copulas for quantile regression with longitudinal data
Journal name Journal of Multivariate Analysis   Check publisher's open access policy
ISSN 0047-259X
1095-7243
Publication date 2016-01-01
Year available 2015
Sub-type Article (original research)
DOI 10.1016/j.jmva.2015.07.004
Open Access Status Not Open Access
Volume 143
Start page 492
End page 502
Total pages 11
Place of publication Maryland Heights, MO, United States
Publisher Academic Press
Collection year 2016
Language eng
Abstract Specifying a correlation matrix is challenging in quantile regression with longitudinal data. A naive method is simply to adopt an independence working model. However, the efficiency of parameter estimates may be lost. We propose constructing a working correlation matrix via Gaussian copula which can handle or incorporate general serial dependence. A suit of unbiased estimating functions can be obtained by assuming the Gaussian copula with different correlation matrices, and the empirical likelihood method can then combine these unbiased estimating functions. Furthermore, the induced smoothing approach is applied to the discontinuous estimating functions to reduce computation burdens. The asymptotic normality of the resulting estimators is established. Simulation studies indicate that the proposed method is superior to the alternative estimating functions especially when the working correlation matrix is misspecified. Finally, a real dataset from forced expiratory volume study is used to illustrate the proposed method.
Keyword Empirical likelihood
Gaussian copula
Induced smoothing
Longitudinal data
Quantile regression
Q-Index Code C1
Q-Index Status Provisional Code
Institutional Status UQ
Additional Notes Published online 21 July 2015

Document type: Journal Article
Sub-type: Article (original research)
Collections: School of Mathematics and Physics
Official 2016 Collection
 
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