期刊名称：Sankhya. Series A, mathematical statistics and probability
印刷版ISSN：0976-836X
电子版ISSN：0976-8378
出版年度：2004
卷号：66
期号：04
出版社：Indian Statistical Institute
摘要：Selection of significant genes via expression patterns is important in a microarray problem. Owing to small sample size and large number of variables (genes), the selection process can be unstable. This paper considers hierarchical Bayesian gene selection model for survival data. In survival analysis the popular models are usually well suited for data with few covariates and many observations subjects). In contrast 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<
