Variable Selection in Finite Mixture of Survival Models

View Researcher's Other Codes

Disclaimer: The provided code links for this paper are external links. Science Nest has no responsibility for the accuracy, legality or content of these links. Also, by downloading this code(s), you agree to comply with the terms of use as set out by the author(s) of the code(s).

Authors Abbas Asgharian, Shili Lin
Journal/Conference Name To be submitted
Paper Category
Paper Abstract V selection in high dimensional spaces has been extensively studied in different settings in the recent statistical literature. In some applications, finite mixture of regression (FMR) models is often used to capture heterogeneity in the population. Variable selection in FMR models has been recently studied. However, such methodologies are not applicable to follow-up studies on time-to-event data when data is incomplete. In this talk, the author will consider variable selection in FMR models when observations are subject to right censoring. The author will propose a penalized likelihood method and study its large sample properties. Simulations are carried out to evaluate the performance of the proposed method and a real data is analyzed for illustrative purposes.
Date of publication 2014
Code Programming Language R

Copyright Researcher 2022