Nonparametric Bayesian estimation based on beta prior in cure model

Xiaobing Zhao*, Cuiliu Xiao, Xian Zhou

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contribution

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Abstract

This paper is to study nonparametric Bayesian estimation for a proportional hazards model with "long-term survivors". The cumulative hazards function is modeled by a beta process, and the priors of the cure rate and coefficient of covariates can be improper distributions under the proposed model. The posterior estimators of the cure rate, the coefficient for covariates and the survival function are estimated from the cases of discrete-time, continuous-time and grouped survival data. A set of leukemia data are re-analyzed to illustrate the proposed model and statistical inference via a Markov chain Monte Carlo (MCMC) algorithm with Gibbs sampling.

Original languageEnglish
Title of host publication3rd International Conference on Bioinformatics and Biomedical Engineering, iCBBE 2009
Place of PublicationPiscataway, N.J
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages1-6
Number of pages6
ISBN (Print)9781424429028
DOIs
Publication statusPublished - 2009
Event3rd International Conference on Bioinformatics and Biomedical Engineering, iCBBE 2009 - Beijing, China
Duration: 11 Jun 200913 Jun 2009

Other

Other3rd International Conference on Bioinformatics and Biomedical Engineering, iCBBE 2009
CountryChina
CityBeijing
Period11/06/0913/06/09

Bibliographical note

Copyright 2009 IEEE. Reprinted from Proceedings, the 3rd International Conference on Bioinformatics and Biomedical Engineering : iCBBE 2009 : June 11-16, 2009 Beijing, China. This material is posted here with permission of the IEEE. Such permission of the IEEE does not in any way imply IEEE endorsement of any of Macquarie University’s products or services. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to pubs-permissions@ieee.org. By choosing to view this document, you agree to all provisions of the copyright laws protecting it.

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