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Empirical Likelihood for Varying Coefficient EV Models under Longitudinal Data 被引量:1
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作者 Qiang LIU 《Acta Mathematicae Applicatae Sinica》 SCIE CSCD 2018年第3期585-596,共12页
In this paper, a varying coefficient errors-in-variables model under longitudinal data is investigated.An empirical likelihood based bias-correction approach is proposed. It is proved that the proposed statistics are ... In this paper, a varying coefficient errors-in-variables model under longitudinal data is investigated.An empirical likelihood based bias-correction approach is proposed. It is proved that the proposed statistics are asymptotically chi-squared under some mild conditions, and hence can be used to construct the confidence regions of the parameters of interest. Finite sample performance of the proposed method is illustrated in a simulation study. The proposed methods are applied to an AIDS clinical trial dataset. 展开更多
关键词 varying coefficient EV model longitudinal data empirical likelihood bias-correction asymptotic normality
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Weighted quantile regression for longitudinal data using empirical likelihood 被引量:1
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作者 YUAN XiaoHui LIN Nan +1 位作者 DONG XiaoGang LIU TianQing 《Science China Mathematics》 SCIE CSCD 2017年第1期147-164,共18页
This paper proposes a new weighted quantile regression model for longitudinal data with weights chosen by empirical likelihood(EL). This approach efficiently incorporates the information from the conditional quantile ... This paper proposes a new weighted quantile regression model for longitudinal data with weights chosen by empirical likelihood(EL). This approach efficiently incorporates the information from the conditional quantile restrictions to account for within-subject correlations. The resulted estimate is computationally simple and has good performance under modest or high within-subject correlation. The efficiency gain is quantified theoretically and illustrated via simulation and a real data application. 展开更多
关键词 empirical likelihood estimating equation influence function longitudinal data weighted quantile regression
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EMPIRICAL LIKELIHOOD APPROACH FOR LONGITUDINAL DATA WITH MISSING VALUES AND TIME-DEPENDENT COVARIATES
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作者 Yan Zhang Weiping Zhang Xiao Guo 《Annals of Applied Mathematics》 2016年第2期200-220,共21页
Missing data and time-dependent covariates often arise simultaneously in longitudinal studies,and directly applying classical approaches may result in a loss of efficiency and biased estimates.To deal with this proble... Missing data and time-dependent covariates often arise simultaneously in longitudinal studies,and directly applying classical approaches may result in a loss of efficiency and biased estimates.To deal with this problem,we propose weighted corrected estimating equations under the missing at random mechanism,followed by developing a shrinkage empirical likelihood estimation approach for the parameters of interest when time-dependent covariates are present.Such procedure improves efficiency over generalized estimation equations approach with working independent assumption,via combining the independent estimating equations and the extracted additional information from the estimating equations that are excluded by the independence assumption.The contribution from the remaining estimating equations is weighted according to the likelihood of each equation being a consistent estimating equation and the information it carries.We show that the estimators are asymptotically normally distributed and the empirical likelihood ratio statistic and its profile counterpart follow central chi-square distributions asymptotically when evaluated at the true parameter.The practical performance of our approach is demonstrated through numerical simulations and data analysis. 展开更多
关键词 empirical likelihood estimating equations longitudinal data missing at random
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