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统计学系系列讲座之313期

 

时间:时间:2018年7月16日(周一)16:00-17:00

地点:史带楼205室

主持人:张新生 教授 复旦大学管理学院统计学系

主题:Regression with covariate subject to limit of detection

主讲人:Prof. 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 regression with left-censored covariate due to the lower limit of detection. 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, we propose a semiparametric likelihood-based approach for the regression parameters using an accelerated failure time model for the covariate subject to limit of detection. A two-stage estimation procedure is considered, 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. The proposed method outperforms the complete case analysis and the substitution methods as well in simulation studies. Asymptotic properties are provided.(This is a joint work with Shengchun Kong.)

 

 

                                                           统计学系 

2018-7-13

 

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