报告题目：Testing missing at random or not with score tests

报告时间：2022年1月18日下午13:00

会议链接：https://meeting.tencent.com/dm/1zD3ZuWxWA1P

会议 ID：822-723-738

主办单位：437必赢会员中心网页版

主讲人：刘玉坤

刘玉坤简介：华东师范大学统计学院教授，博士生导师，统计交叉科学研究院副院长，本科和博士毕业于南开大学统计系。研究兴趣包括经验似然和半参数统计理论及其在缺失数据、偏差数据、生态学、流行病学等方面的应用。主持国家自然科学基金项目4项、科技部重点专项子课题1项，参与重点项目2项。担任中国数学会概率统计学会会刊《应用概率统计》编委和责任编辑、中国科技期刊卓越行动计划高新起点期刊《Statistical Theory and Related Fields》主编助理、以及SCI统计期刊《Journal of Applied Statistics》编委。

报告摘要：Missing data are frequently encountered in various disciplines and can be divided into three categories: missing completely at random (MCAR), missing at random (MAR) and missing not at random (MNAR). Valid statistical approaches to missing data depend crucially on correct  identification of the underlying missingness mechanism. Although the  problem of testing whether this mechanism is MCAR or MAR has been extensively studied, there has been very little research on testing MAR versus MNAR. A critical challenge that is faced when dealing with this problem is the issue of model identification under  MNAR. In this paper, under a logistic model for the missing probability, we develop two   score tests for the problem of whether the missingness mechanism is MAR or MNAR under a parametric model and a semiparametric location model on the regression function. The score tests require only parameter estimation under the null MAR assumption, which completely circumvents the identification issue. The optimalities of the proposed score tests are also discussed.Our simulations and analysis of human immunodeficiency virus data show that the score tests have well-controlled type I errors and desirable powers.