j9九游会

讲座简介：
We develop a doubly robust estimation of generalized partial linear models for longitudinal data with dropouts. Our method extends the highly efficient aggregate unbiased estimating function approach proposed in Qu, Lindsay and Lu (2010) to a doubly robust one in the sense that under missing at random (MAR), our estimator is consistent when either Qu's assumptions (linear conditional mean and constant conditional variance) hold or a model for the dropout process is correctly specified. We begin with a generalized linear model for the marginal mean, and then move forward to a generalized partial linear model, allowing for nonparametric covariate effect by using the regression spline smoothing approximation. We establish the asymptotic theory for the proposed method and use simulation studies to compare its finite sample performance with those of Qu's method, the complete-case generalized estimating equation (GEE) and inverse-probability weighted GEE. The proposed method is finally illustrated using data from a longitudinal cohort study.

报告人简介：
秦国友, 博士,副教授, 硕士生导师.复旦大学公共卫生学院生物统计学教研室主任. 研究偏向：纵向数据剖析
，，，，，半参数模子的稳健推断
，，，，，重大数据统计剖析。
。。。担当中华预防医学会生物统计专业青年委员会主任委员