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  • 统计与管理学院2017年学术报告第44

     

    【主  题】Computationally Efficient Estimation for the Generalized Odds Rate Mixture Cure Model with Interval Censored Data

    【报告人】Jiajia Zhang, 副教授

    University of South Carolina

    【时  间】 2017年06月22日(星期四)14:00-15:00

    【地  点】 上海财经大学统计与管理学院大楼1208室

    【摘  要】For semiparametric survival models with interval censored data and a cure fraction, it is often difficult to derive nonparametric maximum likelihood estimation due

    to the challenge in maximizing the complex likelihood function. In this paper, we propose a computationally efficient EM algorithm, facilitated by a gamma-poisson data augmentation, for maximum likelihood estimation in a class of generalized odds rate mixture cure (GORMC) models with interval censored data. The gamma-poisson data augmentation greatly simplifies the EM estimation and enhances the convergence speed of the EM algorithm. The empirical properties of the proposed method are examined through extensive simulation studies and compared with parametric maximum likelihood estimates. An R package \GORCure" is developed to implement the proposed method and its use is illustrated by an application to the Aerobic Center Longitudinal Study dataset.

    【邀请人】 赵海兵