Lecturer:Wang Qihua

Summary:

Imputation and the inverse probability weighting are two commonly used approaches in missing data analysis. Parametric versions of them are not robust due to model misspecification of some unknown functions. Nonparametric ones are robust but are impractical when the number of covariates is large due to the problem of “curse of dimension”. A beyond multiple robust method is proposed in this paper. This method balances the parametric and nonparametric methods by using some model information contained in the outcome regression function and the selection probability function, and hence alleviates the model misspecification problem and “curse of dimension” problem simultaneously. To illustrate the proposed method, we focus on the estimating problem of response mean in the presence of missing responses. A beyond multiple robust estimator of the response mean is defined, which is proved to be consistent and asymptotically normal as long as one of the true models for the outcome regression or selection probability functions can be some function of its assumed models, without the requirement that one of the true models is correctly specified. Also, it is shown that the asymptotic variance of the proposed estimator is equal to the semiparametric efficiency bound established by Hahn (1998, Econometrica, pp 315-331) when both the selection probability function and the outcome regression function are the functions of their assumed models, respectively. The finite sample properties of the proposed estimator are evaluated by simulation studies and the proposed method is illustrated by a real data analysis.

Introduction to the Lecturer:

Wang Qihua is Research Fellow at the Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Doctoral Supervisor, Recipient of the National Science Fund for Distinguished Young Scholars, and Distinguished Professor of the Chang Jiang Scholars Program under the Ministry of Education. He was selected candidate of the "Hundred Talents Program" of the Chinese Academy of Sciences, and won the First National Hundred Excellent Doctoral Dissertation Award. He had ever taught at Peking University and the University of Hong Kong, and visited Carleton University, University of California, Davis, University of California, Los Angeles, Yale University, University of Washington, Northwestern University, Humboldt-University zu Berlin, Australian National University and University of Sydney. His research interests include survival analysis, missing data analysis, high-dimensional data statistical analysis, and non-semiparametric statistical inference. He published three monographs and more than 100 papers in major international publications including the Annals of Statistics, JASA and Biometrika. He is the Chairman of the CAAS High Dimensional Statistics Society and the Vice Chairman of the Survival Analysis and Biometrics Society, respectively. He serves as a member of the IMS-China and IBS-China Committees successively, and an editorial board member of some international and domestic academic journals.

Time:

11:00-12:00 am, March 30 (Tuesday)

Venue:

Academic Hall #1248, Block B, Zhixin Building, Central Campus

Hosted by the School of Mathematics, Shandong University