Open AccessOptimal unbiased estimation for maximal distribution
Author(s) -
Hanqing Jin,
Shigē Péng
Publication year - 2021
Publication title -
probability, uncertainty and quantitative risk
Language(s) - English
Resource type - Journals
eISSN - 2095-9672
pISSN - 2367-0126
DOI - 10.3934/puqr.2021009
Subject(s) - stein's unbiased risk estimate , minimum variance unbiased estimator , bias of an estimator , estimator , mathematics , efficient estimator , efficiency , statistics , mean squared error , best linear unbiased prediction , consistent estimator , unbiased estimation , u statistic , upper and lower bounds , computer science , mathematical analysis , artificial intelligence , selection (genetic algorithm)
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.