Testing for Homogeneity in Mixture Using Weighted Relative Entropy

DOI10.1080/03610910802305009zbMath1153.62012OpenAlexW1964884967MaRDI QIDQ3543739

Liuxia Wang, Yu-Lin Li

Publication date: 4 December 2008

Published in: Communications in Statistics - Simulation and Computation (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/03610910802305009

zbMATH Keywords

mixturetest for homogeneitymaximum-likelihood estimateKullback-Leibler (K-L) informationWE (weighted relative entropy) test

Mathematics Subject Classification ID

Parametric hypothesis testing (62F03) Statistical aspects of information-theoretic topics (62B10) Asymptotic properties of parametric tests (62F05)

Related Items (2)

Distributions of the Kullback-Leibler divergence with applications ⋮ Improved inequalities for the Poisson and binomial distribution and upper tail quantile functions

Cites Work

• Unnamed Item
• Unnamed Item
• Multi-sample cluster analysis using Akaike's information criterion
• Estimating the dimension of a model
• Testing for the number of components in a mixture of normal distributions using moment estimators
• Asymptotic theory of the likelihood ratio test for the identification of a mixture
• Testing homogeneity in discrete mixtures
• A Modified Likelihood Ratio Test for Homogeneity in Finite Mixture Models
• How Many Clusters? Which Clustering Method? Answers Via Model-Based Cluster Analysis
• An Application of the Laplace Method to Finite Mixture Distributions
• Practical Bayesian Density Estimation Using Mixtures of Normals
• Theory & Methods: Testing for Homogeneity in an Exponential Mixture Model
• Testing Homogeneity in a Mixture Distribution via theL²Distance Between Competing Models
• On Information and Sufficiency
• Likelihood ratio test for univariate Gaussian mixture
• A new look at the statistical model identification

This page was built for publication: Testing for Homogeneity in Mixture Using Weighted Relative Entropy