Optimal unbiased estimation for maximal distribution

IF 1 2区数学 Q3 STATISTICS & PROBABILITY Probability Uncertainty and Quantitative Risk Pub Date : 2016-11-23 DOI:10.3934/puqr.2021009

Hanqing Jin, S. Peng

引用次数: 16

Abstract

Unbiased estimation for parameters of maximal distribution is a fundamental problem in the statistical theory of sublinear expectations. In this paper, we proved that the maximum estimator is the largest unbiased estimator for the upper mean and the minimum estimator is the smallest unbiased estimator for the lower mean.

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最大分布的最优无偏估计

极大分布参数的无偏估计是次线性期望统计理论中的一个基本问题。本文证明了极大估计量是上均值的最大无偏估计量，最小估计量是下均值的最小无偏估计量。

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

Probability Uncertainty and Quantitative Risk STATISTICS & PROBABILITY-

CiteScore

1.60

自引率

13.30%

发文量

审稿时长

12 weeks

期刊介绍： Probability, Uncertainty and Quantitative Risk (PUQR) is a quarterly academic journal under the supervision of the Ministry of Education of the People's Republic of China and hosted by Shandong University, which is open to the public at home and abroad (ISSN 2095-9672; CN 37-1505/O1). Probability, Uncertainty and Quantitative Risk (PUQR) mainly reports on the major developments in modern probability theory, covering stochastic analysis and statistics, stochastic processes, dynamical analysis and control theory, and their applications in the fields of finance, economics, biology, and computer science. The journal is currently indexed in ESCI, Scopus, Mathematical Reviews, zbMATH Open and other databases.

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