中国科学院大学 · 数学 科学学院

1. 学术报告 External Validation of Logistic Regression Models by Testing Homogeneity 时间: 2013年5月24日周五上午10:00-11:00 报告人：Nan Lin (林楠) 地 点：玉泉路教学楼709教室 Dr. Lin is currently an associate professor in Department of Mathematics, College of Arts & Sciences and Division of Biostatistics, School of Medicine at Washington University in St. Louis. He received his Ph.D. in Statistics from University of Illinois at Urbana-Champaign in 2003. His research interests include statistical computing in massive data, Bayesian regularization methods, and statistical applications in bioinformatics, computer science and psychometrics. Abstract: Logistic regression models are widely used in medical diagnosis and prognosis. After the model is established from a training set, it is important to externally validate it using a validation data set for potential changes between the population that the training set is from and that the validation set is from. Currently, Cox’s test combined with some recalibration and model revision techniques has been widely used. In this talk, we discuss an alternative approach based on a novel idea of testing homogeneity. Based on our proposed tests, in the model recalibration stage we can determine when to combine the training and validation data, which can not be decided from Cox’s test. With this new feature, the performance of our updated model outperforms that from existing methods based on Cox’s test. This advantage is demonstrated in the simulation study and the real data study. ***欢迎生物、统计及相关专业感兴趣的老师与同学参加*** 中国科学院大学·数学科学学院