题目：Semiparametric regression analysis of longitudinal skewed data

报告人：林华珍（西南财经大学统计学院教授）

时间：12月2日（星期2）上午9:00-10:00

地点：知新楼B-1248

摘要：In this paper we develop a new semi-parametric regression model for  longitudinal  data. In the new model, we allow the transformation function and the baseline  function to be unknown. The proposed model can provide a much broader class of models than  the existing additive and multiple models. Our estimators for the regression parameters, the transformation function and the baseline function are asymptotically normal, particularly,  the estimators for regression parameters and the transformation function converge to their true values at the rate $n^{-1/2}$, the convergence rate that one could expect for a parametric model. In a simulation study, we demonstrate that the proposed semiparametric method is robust with little loss of efficiency. Finally, we apply the new method to a study on longitudinal health care costs.

林华珍简历：

林华珍，华西医科大学卫生统计专业博士，美国华盛顿大学生物统计系博士后，西南财经大学统计学院教授。

林华珍是国际统计学权威期刊《Biometrics》、《Scandinavian Journal of Statistics》副主编；国内核心学术期刊《应用概率统计》、《系统科学与数学》、《数理统计与管理》编委；美国Math Reveiwer评论员、国家自然科学基金评审专家及国内外三十多个重要刊物审稿人。