统计学系系列讲座之362-363期

统计学系系列讲座之362期

时　间：2019年12月17日（星期二）13:30-14:30

地　点：史带楼802室

主持人：张新生  教授 复旦大学管理学院统计学系

主　题：Semiparametric method for regression with covariate subject to limit of detection: what happens when there is more than one such covariate?

主讲人：Professor Bin Nan Department of Statistics,University of California at Irvine

简　介：Bin Nan 教授是美国统计学会（ASA）和国际数理统计学会（IMS）的Fellow、以及国际统计研究会（ISI）Elected Member。目前担任统计期刊Statistics in Biosciences 和 Lifetime Data Analysis的Associate Editor。在JASA，AOS，AOAS，Biometrika等国际期刊上发表论文超过100篇，他的研究兴趣主要集中在生存分析、高维大脑图像的数据分析、纵向数据的变点分析等研究领域。

摘　要：

We consider generalized linear models with left-censored covariates due to the lower limits of detection. It has been shown that the complete case analysis by eliminating observations with values below limit of detection yields valid estimates for regression coefficients, but loses efficiency; substitution methods are biased; and maximum likelihood method relies on parametric models for the unobservable tail probability, thus may suffer from model misspecification. To obtain robust and more efficient results, a semiparametric pseudo-likelihood approach for the regression parameters using an accelerated failure time model for a single covariate subject to limit of detection was considered in the literature, and a two-stage estimation procedure was proposed, where the conditional distribution of the covariate with limit of detection given other variables is estimated prior to maximizing the likelihood function for the regression parameters. When there are two or more covariates subject to their limits of detection, however, the implementation of the two-stage semiparametric method becomes much more difficult. The added challenge will be discussed in this talk.

统计学系系列讲座之363期

时　间：2019年12月18日（星期三）9:00-11:00

地　点：史带楼301室

主持人：肖志国  副教授 复旦大学管理学院统计学系

主　题：Cost Constraint Machine Learning Models

主讲人：Haoda Fu, Ph.D.

简　介：Dr. Haoda Fu is Enterprise Lead for Machine Learning, Artificial Intelligence, and Digital Connected Care from Eli Lilly and Company, and an Adjunct Professor of Indiana University School of Medicine. Dr Fu received his Ph.D. in statistics from University of Wisconsin-Madison in 2007. He was elected as a Fellow of American Statistical Association in 2019. He has published more than 90 papers in leading statistical and medical journals, and has been teaching topics of machine learning and AI in large industry conferences, including teaching this topic in FDA workshop.

摘　要：

Suppose we can only pay $100 to diagnose a disease subtype for selecting best treatments. We can either measure 10 cheap biomarkers or 2 expensive ones. How can we pick the optimal combinations to achieve highest diagnostic accuracy? Certainly, this is not a trivial problem. In this talk, we will tell a story on how algorithms can make a big difference on computing. Through this talk, you can feel how modern statistics is combined with computer science and algorithms to solve cutting edge 

 统计学系 

2019-12-10