In this paper, empirical likelihood inference for longitudinal data within the framework of partial linear regression models are investigated. The proposed procedures take into consideration the correlation within groups without involving direct estimation of nuisance parameters in the correlation matrix. The empirical likelihood method is used to estimate the regression coefficients and the baseline function, and to construct confidence intervals. A nonparametric version of Wilk's theorem for the limiting distribution of the empirical likelihood ratio is derived. Compared with methods based on normal approximations, the empirical likelihood does not require consistent estimators for the asymptotic variance and bias. The finite sample behaviour of the proposed method is evaluated with simulation and illustrated with an AIDS clinical trial data set.
机构:
Renmin Univ China, Sch Stat, Beijing, Peoples R ChinaRenmin Univ China, Sch Stat, Beijing, Peoples R China
Niu, Cuizhen
Guo, Xu
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Hong Kong Baptist Univ, Dept Math, Hong Kong, Hong Kong, Peoples R China
Nanjing Univ Aeronaut & Astronaut, Coll Econ & Management, Nanjing, Jiangsu, Peoples R ChinaRenmin Univ China, Sch Stat, Beijing, Peoples R China
Guo, Xu
Xu, Wangli
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Renmin Univ China, Sch Stat, Beijing, Peoples R ChinaRenmin Univ China, Sch Stat, Beijing, Peoples R China
Xu, Wangli
Zhu, Lixing
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Hong Kong Baptist Univ, Dept Math, Hong Kong, Hong Kong, Peoples R ChinaRenmin Univ China, Sch Stat, Beijing, Peoples R China
机构:
Capital Univ Econ & Business, Sch Stat, Beijing, Peoples R China
Yancheng Teachers Univ, Sch Math & Stat, Yancheng, Peoples R ChinaCapital Univ Econ & Business, Sch Stat, Beijing, Peoples R China
Sun, Huihui
Liu, Qiang
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Capital Univ Econ & Business, Sch Stat, Beijing, Peoples R China
Capital Univ Econ & Business, Sch Stat, Beijing 100070, Peoples R ChinaCapital Univ Econ & Business, Sch Stat, Beijing, Peoples R China