最小散度估计器、最大似然估计与广义自助法
Minimum Divergence Estimators, Maximum Likelihood and the Generalized Bootstrap.
作者信息
Broniatowski Michel
机构信息
Faculté de Mathématiques, Laboratoire de Probabilité, Statistique et Modélisation, Université Pierre et Marie Curie (Sorbonne Université), 4 Place Jussieu, CEDEX 05, 75252 Paris, France.
出版信息
Entropy (Basel). 2021 Jan 31;23(2):185. doi: 10.3390/e23020185.
Abstract
This paper states that most commonly used minimum divergence estimators are MLEs for suited generalized bootstrapped sampling schemes. Optimality in the sense of Bahadur for associated tests of fit under such sampling is considered.
摘要
本文指出，对于合适的广义自助抽样方案，最常用的最小散度估计量是极大似然估计量。本文考虑了在此类抽样下相关拟合优度检验在巴哈杜尔意义下的最优性。
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