Bayesian nonparametric k-sample tests for censored and uncensored data
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.
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- Hanson, Timothy E., 2006. "Inference for Mixtures of Finite Polya Tree Models," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 1548-1565, December.
- Martínez-Camblor, Pablo & de Uña-Álvarez, Jacobo, 2009. "Non-parametric k-sample tests: Density functions vs distribution functions," Computational Statistics & Data Analysis, Elsevier, vol. 53(9), pages 3344-3357, July.
- Michael P. Fay & Pamela A. Shaw, . "Exact and Asymptotic Weighted Logrank Tests for Interval Censored Data: The interval R Package," Journal of Statistical Software, American Statistical Association, vol. 36(i02).
- Bharath, Karthik & Dey, Dipak K., 2011. "Test to distinguish a Brownian motion from a Brownian bridge using Polya tree process," Statistics & Probability Letters, Elsevier, vol. 81(1), pages 140-145, January.
- Hanson T. & Johnson W.O., 2002. "Modeling Regression Error With a Mixture of Polya Trees," Journal of the American Statistical Association, American Statistical Association, vol. 97, pages 1020-1033, December.
- Zhang, Jin & Wu, Yuehua, 2007. "k-Sample tests based on the likelihood ratio," Computational Statistics & Data Analysis, Elsevier, vol. 51(9), pages 4682-4691, May.
- Timothy E. Hanson & Athanasios Kottas & Adam J. Branscum, 2008. "Modelling stochastic order in the analysis of receiver operating characteristic data: Bayesian non-parametric approaches," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 57(2), pages 207-225.
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