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

JOURNAL OF MULTIVARIATE ANALYSIS

卷:143

Article

Fu, Liya^1,3  Wang, You-Gan^2,3 

[1] Xi An Jiao Tong Univ, Sch Math & Stat, Xian, Peoples R China

[2] Queensland Univ Technol, Sch Math Sci, Brisbane, Qld 4001, Australia

[3] Univ Queensland, Sch Math & Phys, Ctr Applicat Nat Resource Math, Brisbane, Qld 4072, Australia

关键词: Empirical likelihood;    Gaussian copula;    Induced smoothing;    Longitudinal data;    Quantile regression;   

DOI  :  10.1016/j.jmva.2015.07.004

来源: Elsevier

【 摘 要 】

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. (C) 2015 Elsevier Inc. All rights reserved.

【 授权许可】

Free