A special feature of Probability and Statistics Day at UMBC 2014 is that the conference, including the workshop, is open to all statistics graduate students
from UMBC and local universites free of charge; however, REGISTRATION IS REQUIRED! The deadline to register is Friday,
April 11, 2014. // REGISTER NOW
For more information, contact any member of the organizing committee:
Bimal Sinha
Conference Chair
443.538.3012
Kofi Adragni
410.455.2406
Yvonne Huang
410.455.2422
Yaakov Malinovsky
410.455.2968
Thomas Mathew
410.455.2418
Nagaraj Neerchal
410.455.2437
DoHwan Park
410.455.2408
Junyong Park
410.455.2407
Anindya Roy
410.455.2435
Elizabeth Stanwyck
410.455.5731
Participant Information
Shoubhik Mondal
Paper: Model Assisted Cox Regression
Semiparametric random censorship (SRC) models (Dikta, 1998), derive their rationale from their ability to gainfully utilize parametric ideas within the random censorship environment. An extension of this approach is developed for Cox regression, producing new estimators of the regression parameter and baseline cumulative hazard function. Under correct parametric specification, the proposed estimator of the regression parameter is shown to be asymptotically more efficient than the standard partial likelihood estimator. Numerical studies are presented to showcase the efficacy of the proposed approach even under significant misspecification. A real example is provided. A further extension to the case of missing censoring indicators is also developed and an illustration with pseudo-real data is provided.
Poster: Model Assisted Cox Regression
Semiparametric random censorship (SRC) models (Dikta, 1998), derive their rationale from their ability to gainfully utilize parametric ideas within the random censorship environment. An extension of this approach is developed for Cox regression, producing new estimators of the regression parameter and baseline cumulative hazard function. Under correct parametric specification, the proposed estimator of the regression parameter is shown to be asymptotically more efficient than the standard partial likelihood estimator. Numerical studies are presented to showcase the efficacy of the proposed approach even under significant misspecification. A real example is provided. A further extension to the case of missing censoring indicators is also developed and an illustration with pseudo-real data is provided.
