ON SOME OPTIMAL BAYESIAN NONPARAMETRIC RULES FOR ESTIMATING DISTRIBUTION FUNCTIONS

被引：4
|
作者：
Ruggeri, Fabrizio [1 ]
机构：
[1] CNR IMATI, I-20133 Milan, Italy
来源：
ECONOMETRIC REVIEWS | 2014年 / 33卷 / 1-4期
关键词：
Bayesian analysis; Dirichlet process; -minimax; Posterior regret; C11;
D O I：
10.1080/07474938.2013.807183
中图分类号：
F [经济];
学科分类号：
02 ;
摘要：
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 (?, A), with parameter 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.
引用
收藏
页码：289 / 304
页数：16
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