题目： Estimation and variable selection in generalized partially nonlinear models with nonignorable missing responses

报告人：唐年胜 教授 （云南大学数学与统计学院）

Abstract : Based on the local kernel estimation method and propensity score adjustment method, we develop a penalized likelihood approach to simultaneously select covariates and explanatory variables in the considered parametric respondent model, and estimate parameters and nonparametric functions in generalized partially nonlinear models with nonignorable missing responses. An EM algorithm is proposed to evaluate the penalized likelihood estimations of parameters. The IC$_Q$ criterion is employed to select the optimal penalty parameter. Under some regularity conditions, we show some asymptotic properties of parameter estimators such as oracle property. It can be shown that the proposed  local linear kernel estimator of the nonparametric component is an estimator of a least favorable curve. The consistency of the IC$_Q$-based selection procedure is obtained. Simulation studies are conducted, and a real data set is used to illustrate the proposed methodologies.

时间： 11月2日（周五）15:00-16:00

地点：565net必赢客户端本部教二楼 527 教室

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