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Bayesian nonparametric k-sample tests for censored and uncensored data

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  • Chen, Yuhui
  • Hanson, Timothy E.

Abstract

Polya tree priors are random probability distributions that are easily centered at standard parametric families, such as the normal. As such, they provide a convenient avenue toward creating a parametric/nonparametric test statistic “blend” for the classic problem of testing whether data distributions are the same across several subpopulations. Test-statistics that are (empirical) Bayes factors constructed from independent Polya tree priors are proposed. The Polya tree centering distributions are Gaussian with parameters estimated from the data and the p-values are obtained through the permutation of group membership indicators. Generalizations to censored and multivariate data are provided. The conceptually simple test statistic fares surprisingly well against competitors in simulations.

Suggested Citation

  • Chen, Yuhui & Hanson, Timothy E., 2014. "Bayesian nonparametric k-sample tests for censored and uncensored data," Computational Statistics & Data Analysis, Elsevier, vol. 71(C), pages 335-346.
    • Handle: RePEc:eee:csdana:v:71:y:2014:i:c:p:335-346
    DOI: 10.1016/j.csda.2012.11.003
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    Cited by:

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    3. Luai Al-Labadi, 2021. "The two-sample problem via relative belief ratio," Computational Statistics, Springer, vol. 36(3), pages 1791-1808, September.
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    6. 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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