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Empirical likelihood for nonlinear regression models with nonignorable missing responses
The Canadian Journal of Statistics ( IF 0.6 ) Pub Date : 2020-03-02 , DOI: 10.1002/cjs.11540
Zhihuang Yang 1 , Niansheng Tang 1
Affiliation  

This article develops three empirical likelihood (EL) approaches to estimate parameters in nonlinear regression models in the presence of nonignorable missing responses. These are based on the inverse probability weighted (IPW) method, the augmented IPW (AIPW) method and the imputation technique. A logistic regression model is adopted to specify the propensity score. Maximum likelihood estimation is used to estimate parameters in the propensity score by combining the idea of importance sampling and imputing estimating equations. Under some regularity conditions, we obtain the asymptotic properties of the maximum EL estimators of these unknown parameters. Simulation studies are conducted to investigate the finite sample performance of our proposed estimation procedures. Empirical results provide evidence that the AIPW procedure exhibits better performance than the other two procedures. Data from a survey conducted in 2002 are used to illustrate the proposed estimation procedure. The Canadian Journal of Statistics 48: 386–416; 2020 © 2020 Statistical Society of Canada

中文翻译:

具有不可忽略的缺失响应的非线性回归模型的经验似然

本文开发了三种经验似然(EL)方法,以在存在不可忽略的缺失响应的情况下估计非线性回归模型中的参数。这些基于逆概率加权(IPW)方法,增强IPW(AIPW)方法和归因技术。采用逻辑回归模型指定倾向得分。通过结合重要性采样和估算方程的估算,最大似然估算可用于估算倾向得分中的参数。在某些规律性条件下,我们获得了这些未知参数的最大EL估计量的渐近性质。进行模拟研究以调查我们提出的估计程序的有限样本性能。实证结果提供了证据,表明AIPW程序比其他两个程序表现出更好的性能。2002年进行的一项调查的数据用于说明建议的估算程序。《加拿大统计杂志》 48:386–416;2020©2020加拿大统计学会
更新日期:2020-03-02
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