Application of Mixture Model for Multiple Testing
Date Issued
2005
Date
2005
Author(s)
Wei, Yu-Chung
DOI
zh-TW
Abstract
Current research about multiple hypotheses testing has focused more on the development of strategies in order to increase the overall power. Most approaches try to identify a number when deciding the threshold for p values. If only a fixed proportion of the null hypotheses are true, then for each hypothesis, it may have a certain probability of being significant. Here I adopt the mixture model to account for the uncertainty. In other words, after considering the possibility of being true and false, the test statistic becomes a weighted average with the weight equals to the probability that the hypothesis is true. The value of the threshold is then determined based on the estimate of the proportion. This procedure can be applied to frequentisits’ approach using p values or Bayesians’ Bayes factors. Simulations studies are conducted to assess the performance of the proposed procedure and comparisons are made with the traditional Bonferroni’s procedure and Benjamini-Hochberg (BH) procedure. It appears that this proposal has a smaller overall type I error, and achieves almost the same power as BH procedure.
Subjects
多重檢定
混合模式
最大概似估計值
後驗分配眾數值
p值
貝氏因子
Multiple Testing
Mixture Model
Maximum Likelihood Estimator(MLE)
Posterior Mode
p-Value
Bayes Factor
Type
thesis
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