Title : ( Mixture Models in View of Evidential Analysis )
Authors: Mahdi Emadi ,
Full TextAbstract
In some practical inferential situations, it is needed to mix some finite sort of distributions to fit an adequate model for multi-modal observations. In this article, using evidential analysis, we determine the sample size for supporting hypotheses about the mixture proportion and homogeneity. An Expectation-Maximization algorithm is used to evaluate the probability of strong misleading evidence based on modified likelihood ratio as a measure of support.
Keywords
Finite mixture distribution; Modi ed likelihood ratio; Penalty term; Statistical evidence; Strong misleading evidence
@article{paperid:1031575,
author = {Emadi, Mahdi},
title = {Mixture Models in View of Evidential Analysis},
journal = {Communications in Statistics Part B: Simulation and Computation},
year = {2013},
volume = {42},
number = {4},
month = {March},
issn = {0361-0918},
pages = {1--12},
numpages = {11},
keywords = {Finite mixture distribution; Modi ed likelihood ratio; Penalty term; Statistical evidence; Strong misleading evidence},
}
%0 Journal Article
%T Mixture Models in View of Evidential Analysis
%A Emadi, Mahdi
%J Communications in Statistics Part B: Simulation and Computation
%@ 0361-0918
%D 2013
