关于一维模糊随机变量的极大似然估计方法(英文)

A method of maxi mumlikelihood esti mation for one di mensional fuzzy randomvariables

研究一维模糊随机变量情形总体未知参数的极大似然估计的两种方法———扩张原理法与随机集的可积选择法.在模糊观测条件下,定义了Kwakernaak-Kruse-Meyer型模糊随机变量情形的模糊参数的极大似然估计量及Puri-Ralescu型变量情形的模糊参数的边缘极大似然估计量.得到了它们的存在条件,一致性条件及其相关性质.

Some maximum likelihood estimation （MLE） methods for unknown parameters of the population under fuzzy observation are proposed in this paper. This investigation is focused on MLE via Zadeh＇s extension principle and integrable selections of random set. We have obtained conditions of existence and consistency of MLE respectively, also a characterization with their relevant properties.