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Activity Number:
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197
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Type:
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Contributed
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Date/Time:
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Monday, July 30, 2007 : 2:00 PM to 3:50 PM
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Sponsor:
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Biometrics Section
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| Abstract - #309382 |
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Title:
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Parametric Mixture Model for Survival Data
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Author(s):
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Ying Zhang*+ and Jagbir Singh
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Companies:
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Wyeth and Temple University
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Address:
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, Collegeville, PA, 19426,
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Keywords:
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Parametric mixture model ; EM algorithm ; Maximum Likelihood Estimation ; Model selection
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Abstract:
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The analysis of failure-time data is usually complicated by the presence of censoring so that the regular estimation methods need to be modified. In this talk, we propose a parametric mixture model and its estimation based on maximum likelihood. An Expectation-Maximization (EM) algorithm is implemented to achieve the maximum likelihood estimation of the parametric mixture model for survival data. Furthermore, we develop a statistic based on Bayesian Information Criterion (BIC) for model selection. The parametric mixture model is tested on both simulated data and real data. It is seen to work reasonably well, provides great flexibility and easy to interpret. Since the maximum likelihood method is used to estimate the parametric mixture model, the statistical inference is not difficult to follow.
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- The address information is for the authors that have a + after their name.
- Authors who are presenting talks have a * after their name.
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