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Bayesian nonparametric binary regression via random tessellations

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  • Trippa, Lorenzo
  • Muliere, Pietro

Abstract

A Bayesian nonparametric model for binary random variables is introduced. The characterization of the probability model is based on the Dirichlet process and on the Poisson hyperplane tessellation model. These two stochastic models are combined in order to adapt, under the hypothesis of partial exchangeability, the reinforcement mechanism of the Pólya urn scheme. A Gibbs sampling algorithm for implementing predictive inference is illustrated and an application of the inferential procedure is discussed.

Suggested Citation

  • Trippa, Lorenzo & Muliere, Pietro, 2009. "Bayesian nonparametric binary regression via random tessellations," Statistics & Probability Letters, Elsevier, vol. 79(21), pages 2273-2280, November.
    • Handle: RePEc:eee:stapro:v:79:y:2009:i:21:p:2273-2280
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    1. De Iorio, Maria & Muller, Peter & Rosner, Gary L. & MacEachern, Steven N., 2004. "An ANOVA Model for Dependent Random Measures," Journal of the American Statistical Association, American Statistical Association, vol. 99, pages 205-215, January.
    2. Griffin, J.E. & Steel, M.F.J., 2006. "Order-Based Dependent Dirichlet Processes," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 179-194, March.
    3. Muliere, Pietro & Secchi, Piercesare & Walker, Stephen, 2005. "Partially exchangeable processes indexed by the vertices of a k-tree constructed via reinforcement," Stochastic Processes and their Applications, Elsevier, vol. 115(4), pages 661-677, April.
    4. Carter, C.K. & Kohn, R., "undated". "Robust Bayesian nonparametric regression," Statistics Working Paper _004, Australian Graduate School of Management.
    5. Walker, Stephen & Muliere, Pietro, 2003. "A bivariate Dirichlet process," Statistics & Probability Letters, Elsevier, vol. 64(1), pages 1-7, August.
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