This is the program for the 2010 Joint Statistical Meetings in Vancouver, British Columbia.

Abstract Details

Activity Number: 588
Type: Contributed
Date/Time: Wednesday, August 4, 2010 : 2:00 PM to 3:50 PM
Sponsor: Section on Risk Analysis
Abstract - #306644
Title: Inference for Interval-Censored Data from Multistate Models
Author(s): Yang Yang*+ and Vijay Nair
Companies: University of Michigan and University of Michigan
Address: 439 West Hall , Ann Arbor, MI, 48109-1107,
Keywords: multi-state model ; Markov and semi-Markov model ; interval censoring ; right censoring ; data augmentation
Abstract:

Modeling of reliability, survival, finance, and other event-history data as using multi-state models is becoming increasingly common. However, the statistical inference problem is complicated in the presence of censoring, especially interval censoring, and there is only limited work beyond Markov models. Likelihood estimation from interval censored sample relies heavily on sojourn time assumption, so it is hard to extend to semi-Markov models. Besides, when model involves complex structure, chances are that some transitions are unobserved. To conquer these two obstacles, this presentation will develop Bayesian parametric inference for semi-Markov models using data augmentation. Multi-state models with a progressive structure are considered and general algorithm proposed. Properties of the estimators will be examined and compared with MLEs which can be computed in simple cases.


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