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

[Math. Dept.]

September 13, 2018  14:30-15:30

E409  School of Mathematics

[colloquium] Niansheng Tang20180913-01.png

 SPEAKER

唐年胜（云南大学）

 ABSTRCT

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.

 SUPPORTED BY

School of Mathematics, Sichuan University