On Some Optimal Bayesian Nonparametric Rules for Estimating Distribution Functions

Fabrizio Ruggeri

Econometric Reviews, 2014, vol. 33, issue 1-4, 289-304

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.

Date: 2014
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DOI: 10.1080/07474938.2013.807183

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