A parametric model to estimate the proportion from true null using a distribution for p-values

Chang Yu and Daniel Zelterman

Computational Statistics & Data Analysis, 2017, vol. 114, issue C, 105-118

Abstract: Microarray studies generate a large number of p-values from many gene expression comparisons. The estimate of the proportion of the p-values sampled from the null hypothesis draws broad interest. The two-component mixture model is often used to estimate this proportion. If the data are generated under the null hypothesis, the p-values follow the uniform distribution. What is the distribution of p-values when data are sampled from the alternative hypothesis? The distribution is derived for the chi-squared test. Then this distribution is used to estimate the proportion of p-values sampled from the null hypothesis in a parametric framework. Simulation studies are conducted to evaluate its performance in comparison with five recent methods. Even in scenarios with clusters of correlated p-values and a multicomponent mixture or a continuous mixture in the alternative, the new method performs robustly. The methods are demonstrated through an analysis of a real microarray dataset.

Keywords: Distribution of p-values; Microarray studies; Mixture model; Proportion from the null hypothesis (search for similar items in EconPapers)
Date: 2017
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:114:y:2017:i:c:p:105-118

DOI: 10.1016/j.csda.2017.04.008

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