An asymptotically efficient closed-form estimator for the Dirichlet distribution

IF 0.7 4区数学 Q3 STATISTICS & PROBABILITY

Stat Pub Date : 2023-12-13 DOI:10.1002/sta4.640

Jae Ho Chang, Sang Kyu Lee, Hyoung-Moon Kim

引用次数: 0

Abstract

Maximum likelihood estimator (MLE) of the Dirichlet distribution is usually obtained by using the Newton–Raphson algorithm. However, in some cases, the computational costs can be burdensome, for example, in real-time processes. Therefore, it is beneficial to develop a closed-form estimator that is as efficient as the MLE for large sample. Here, we suggest asymptotically efficient closed-form estimator based on the classical large sample theory.

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迪里夏特分布的渐近有效闭式估计器

狄利克雷分布的极大似然估计量通常是用Newton-Raphson算法求得的。然而，在某些情况下，计算成本可能是繁重的，例如，在实时进程中。因此，对于大样本，开发一种与最大似然估计一样有效的封闭估计是有益的。本文在经典大样本理论的基础上，提出了渐近有效闭型估计量。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Stat Decision Sciences-Statistics, Probability and Uncertainty

CiteScore

1.10

自引率

0.00%

发文量

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