Semiparametric Bayesian analysis of high-dimensional censored outcome data

Semiparametric Bayesian analysis of high-dimensional censored outcome data

The Surveillance, Epidemiology and End Results (SEER) cancer database contains survival data for US individuals diagnosed with cancer. Semiparametric Bayesian methods are computationally expensive to fit for such large data-sets. This paper develops a cost-effective Markov chain Monte Carlo strategy...

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Bibliographic Details
Main Authors: Chetkar Jha, Yi Li, Subharup Guha
Format: Article
Language:English
Published: Taylor & Francis Group 2017-07-01
Series:Statistical Theory and Related Fields
Subjects:
icar models
data squashing
dirichlet process
generalised pólya urn process
big data
Online Access:http://dx.doi.org/10.1080/24754269.2017.1396436

Internet

http://dx.doi.org/10.1080/24754269.2017.1396436

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