缺失数据下带固定效应的半参数变系数模型的经验似然估计OA

The statistical inference problem for semiparametric varying coefficient models with fixed effects under missing data is addressed.The varying coefficient functions are handled through local poly-nomial methods,and fixed effects are eliminated through instrumental variables.The issue of missing da-ta is resolved by employing the Kaplan-Meier product limit estimator,and a semiparametric empirical likelihood ratio statistic is constructed.In appropriate regularity conditions,the proposed estimators are proved to be consistent and asymptotically normal,and the empirical likelihood ratio statistic is shown to follow the standard chi-squared distribution.Monte Carlo simulations reveal that compared with conven-tional complete-case analysis,the empirical likelihood inference can effectively reduce bias and enhance estimation efficiency.