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On Some Optimal Bayesian Nonparametric Rules for Estimating Distribution Functions

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  • Fabrizio Ruggeri

Abstract

In this paper, we present a novel approach to estimating distribution functions, which combines ideas from Bayesian nonparametric inference, decision theory and robustness. Given a sample from a Dirichlet process on the space (𝒳, A), with parameter η in a class of measures, the sampling distribution function is estimated according to some optimality criteria (mainly minimax and regret), when a quadratic loss function is assumed. Estimates are then compared in two examples: one with simulated data and one with gas escapes data in a city network.

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  • Fabrizio Ruggeri, 2014. "On Some Optimal Bayesian Nonparametric Rules for Estimating Distribution Functions," Econometric Reviews, Taylor & Francis Journals, vol. 33(1-4), pages 289-304, June.
    • Handle: RePEc:taf:emetrv:v:33:y:2014:i:1-4:p:289-304
    DOI: 10.1080/07474938.2013.807183
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    1. James Berger & Elías Moreno & Luis Pericchi & M. Bayarri & José Bernardo & Juan Cano & Julián Horra & Jacinto Martín & David Ríos-Insúa & Bruno Betrò & A. Dasgupta & Paul Gustafson & Larry Wasserman &, 1994. "An overview of robust Bayesian analysis," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 3(1), pages 5-124, June.
    2. Stephen G. Walker & Paul Damien & PuruShottam W. Laud & Adrian F. M. Smith, 1999. "Bayesian Nonparametric Inference for Random Distributions and Related Functions," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 61(3), pages 485-527.
    3. Antonio Pievatolo & Fabrizio Ruggeri, 2004. "Bayesian reliability analysis of complex repairable systems," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 20(3), pages 253-264, July.
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    Cited by:

    1. Ali Karimnezhad & Mahmoud Zarepour, 2020. "A general guide in Bayesian and robust Bayesian estimation using Dirichlet processes," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 83(3), pages 321-346, April.

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