時 間：114年11月20日（星期四下午14:10~15:00）
地 點：管理學院新大樓M103
主講人：陳福錄 教授
(Department of Mathematics, College of Natural Science, Can Tho University, Can Tho, Vietnam)
講 題：Large-sample properties of multiple imputation estimators for parameters of logistic regression with covariates missing at random separately or simultaneously
Abstract
We examine the asymptotic properties of two multiple imputation (MI) estimators, given in the study of Lee et al. (2023), for the parameters of logistic regression with both sets of discrete or categorical covariates that are missing at random separately or simultaneously. The proposed estimated asymptotic variances of the two MI estimators address a limitation observed with Rubin's estimated variances, which lead to underestimate the variances of the two MI estimators (Rubin 1987). Simulation results demonstrate that our two proposed MI methods outperform the complete-case, semiparametric inverse probability weighting, random forest MI using chained equations, and stochastic approximation of expectation-maximization methods. To illustrate the methodology's practical application, we provide a real data example from a survey conducted at the Feng Chia night market in Taichung City, Taiwan.
Keywords: Inverse probability weighting; Logistic regression; missing at random; Multiple imputation.