Maximum likelihood estimation from fuzzy data using the EM algorithm

被引：89
|
作者：
Denoeux, Thierry [1 ]
机构：
[1] Univ Technol Compiegne, CNRS, UMR Heudiasyc 6599, F-60205 Compiegne, France
来源：
FUZZY SETS AND SYSTEMS | 2011年 / 183卷 / 01期
关键词：
Statistics; Fuzzy data analysis; Estimation; Maximum likelihood principle; Regression; Mixture models; RANDOM-VARIABLES; MIXTURE MODEL; REGRESSION;
D O I：
10.1016/j.fss.2011.05.022
中图分类号：
TP301 [理论、方法];
学科分类号：
081202 ;
摘要：
A method is proposed for estimating the parameters in a parametric statistical model when the observations are fuzzy and are assumed to be related to underlying crisp realizations of a random sample. This method is based on maximizing the observed-data likelihood defined as the probability of the fuzzy data. It is shown that the EM algorithm may be used for that purpose, which makes it possible to solve a wide range of statistical problems involving fuzzy data. This approach, called the fuzzy EM (FEM) method, is illustrated using three classical problems: normal mean and variance estimation from a fuzzy sample, multiple linear regression with crisp inputs and fuzzy outputs, and univariate finite normal mixture estimation from fuzzy data. (C) 2011 Elsevier B.V. All rights reserved.
引用
收藏
页码：72 / 91
页数：20
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