Two‐group Poisson‐Dirichlet mixtures for multiple testing
| Autor: | Michele Guindani, Antonio Lijoi, Fabrizio Leisen, Francesco Denti, Marina Vannucci, William Duncan Wadsworth |
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| Přispěvatelé: | Denti, F, Guindani, M, Leisen, F, Lijoi, A, Wadsworth, W, Vannucci, M |
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Statistics and Probability
TWO-GROUP MODEL Computer science MICROBIOME ANALYSIS MULTIPLE TESTING Inference Probability density function Poisson distribution 01 natural sciences Article General Biochemistry Genetics and Molecular Biology Dirichlet distribution 010104 statistics & probability 03 medical and health sciences symbols.namesake Bayesian nonparametric POISSON-DIRICHLET PROCESS Humans Applied mathematics Computer Simulation 0101 mathematics Child microbiome analysi 030304 developmental biology Statistical hypothesis testing Poisson-Dirichlet proce 0303 health sciences General Immunology and Microbiology Microbiota Applied Mathematics Bayes Theorem Markov chain Monte Carlo General Medicine Markov Chains Data set Settore SECS-S/01 - STATISTICA Multiple comparisons problem symbols BAYESIAN NONPARAMETRICS MICROBIOME ANALYSIS MULTIPLE TESTING POISSON-DIRICHLET PROCESS TWO-GROUP MODEL General Agricultural and Biological Sciences BAYESIAN NONPARAMETRICS Monte Carlo Method |
| Zdroj: | Biometrics |
| Popis: | The simultaneous testing of multiple hypotheses is common to the analysis of high-dimensional data sets. The two-group model, first proposed by Efron, identifies significant comparisons by allocating observations to a mixture of an empirical null and an alternative distribution. In the Bayesian nonparametrics literature, many approaches have suggested using mixtures of Dirichlet Processes in the two-group model framework. Here, we investigate employing mixtures of two-parameter Poisson-Dirichlet Processes instead, and show how they provide a more flexible and effective tool for large-scale hypothesis testing. Our model further employs nonlocal prior densities to allow separation between the two mixture components. We obtain a closed-form expression for the exchangeable partition probability function of the two-group model, which leads to a straightforward Markov Chain Monte Carlo implementation. We compare the performance of our method for large-scale inference in a simulation study and illustrate its use on both a prostate cancer data set and a case-control microbiome study of the gastrointestinal tracts in children from underdeveloped countries who have been recently diagnosed with moderate-to-severediarrhea. |
| Databáze: | OpenAIRE |
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