2018年08月22日 |  English version
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双周三学术报告会:Case Deletion Measures for Statistical Models with Missing Data

报告题目:Case Deletion Measures for Statistical Models with Missing Data

报告人:解锋昌 教授

报告时间:20184月18日(周三)14:00

报告地点:行健楼学术活动室526

摘要:We focus on the case-deletion measures for statistical models with missing data. Cook’s distance is one of the most important diagnostic tools to identify influential observations on the parametric models. However, Cook’s distance may not be directly comparable because its scale stochastically depends on the degree of the perturbation. We define the degree of perturbation for statistical models with missing data. Then, we derive the Cook’s distance based on the $Q$-function used in the EM algorithm for models with missing data. We further develop the scaled Cook’s distance in the statistical models with missing data, which resolves the size issue of Cook’s distance. Simulation studies are used to illustrate the size matters issue in models with missing data. The application of case deletion measures is examined in one real data example.

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