学术报告题目： Doubly robust kernel smoothing density estimation when group membership is missing at random

报告人：唐万副教授（美国杜兰大学公共卫生学院）

报告时间： 2019年12月25日（星期三）15:30-16:30

报告地点：新图书馆会议中心五会议室

报告摘要: The density function is a fundamental concept in data analysis. When a population consists of heterogeneous subjects, it is often of great interest to estimate the density functions of the subpopulations. Nonparametric methods such as kernel smoothing estimates may be applied to each subpopulation to estimate the density functions if there are no missing values. In situations where the membership for a subpopulation is missing, kernel smoothing estimates using only subjects with membership available are valid only under missing complete at random (MCAR). In this talk, I will present a doubly robust kernel smoothing methods for density function estimates by combining models of the missing mechanism and prediction models of the membership under the missing at random (MAR) assumption. The asymptotic properties of the new estimates are developed, and simulation studies and a real study in mental health are used to illustrate the performance of the new estimates.