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Bayesian nonparametric multiple testing

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  • Cipolli III, William
  • Hanson, Timothy
  • McLain, Alexander C.

Abstract

Multiple testing, or multiplicity problems often require testing several means with the assumption of rejecting infrequently, as motivated by the need to analyze DNA microarray data. The goal is to keep the combined rate of false discoveries and non-discoveries as small as possible. A discrete approximation to a Polya tree prior that enjoys fast, conjugate updating, centered at the usual Gaussian distribution is proposed. This new technique and the advantages of this approach are demonstrated using extensive simulation and data analysis accompanied by a Java web application. The numerical studies demonstrate that this new procedure shows promising false discovery rate and estimation of key values in the mixture model with very reasonable computational speed.

Suggested Citation

  • Cipolli III, William & Hanson, Timothy & McLain, Alexander C., 2016. "Bayesian nonparametric multiple testing," Computational Statistics & Data Analysis, Elsevier, vol. 101(C), pages 64-79.
    • Handle: RePEc:eee:csdana:v:101:y:2016:i:c:p:64-79
    DOI: 10.1016/j.csda.2016.02.016
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    References listed on IDEAS

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    Cited by:

    1. Feng, Long & Zhang, Xiaoxu & Liu, Binghui, 2020. "A high-dimensional spatial rank test for two-sample location problems," Computational Statistics & Data Analysis, Elsevier, vol. 144(C).
    2. Luz Adriana Pereira & Daniel Taylor‐Rodríguez & Luis Gutiérrez, 2020. "A Bayesian nonparametric testing procedure for paired samples," Biometrics, The International Biometric Society, vol. 76(4), pages 1133-1146, December.
    3. William Cipolli & Timothy Hanson, 2019. "Supervised learning via smoothed Polya trees," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 13(4), pages 877-904, December.

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