Proceedings of the Korean Statistical Society Conference (한국통계학회:학술대회논문집)
- 2005.05a
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- Pages.17-23
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- 2005
Bayesian Variable Selection in the Proportional Hazard Model with Application to Microarray Data
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Lee, Kyeong-Eun
(Department of Statistics, Kyungpook National University) ;
- Mallick, Bani K. (Department of Statistics, Texas A&M University)
- Published : 2005.05.20
Abstract
In this paper we consider the well-known semiparametric proportional hazards models for survival analysis. These models are usually used with few covariates and many observations (subjects). But, for a typical setting of gene expression data from DNA microarray, we need to consider the case where the number of covariates p exceeds the number of samples n. For a given vector of response values which are times to event (death or censored times) and p gene expressions(covariates), we address the issue of how to reduce the dimension by selecting the significant genes. This approach enables us to estimate the survival curve when n
Keywords
- Bayesian Model;
- Cox Proportional Hazards Model;
- Microarray Data;
- Survival analysis;
- Variable Selection