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The performance of mutual information for mixture of bivariate normal disatributions based on robust kernel estimation.

Dadkhah, Kourosh and Midi, Habshah (2010) The performance of mutual information for mixture of bivariate normal disatributions based on robust kernel estimation. Applied Mathematical Sciences, 4 (29). pp. 1417-1436. ISSN 1312-885X

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Abstract

Mutual Information (MI) measures the degree of association between variables in nonlinear model as well as linear models. It can also be used to measure the dependency between variables in mixture distribution. The MI is estimated based on the estimated values of the joint density function and the marginal density functions of X and Y. A variety of methods for the estimation of the density function have been recommended. In this paper, we only considered the kernel method to estimate the density function. However, the classical kernel density estimator is not reliable when dealing with mixture density functions which prone to create two distant groups in the data. In this situation a robust kernel density estimator is proposed to acquire a more efficient MI estimate in mixture distribution. The performance of the robust MI is investigated extensively by Monte Carlo simulations. The results of the study offer substantial improvement over the existing techniques.

Item Type:Article
Keyword:Mutual information; Kernel density, Minimum volume El- lipsoid; Minimum covariance determinant; Outliers; Mixture distribution;Robust statistics
Subject:Robust statistics.
Subject:Information theory.
Subject:Probabilities.
Faculty or Institute:Faculty of Science
Publisher:Hikari Ltd
ID Code:17263
Deposited By: Najwani Amir Sariffudin
Deposited On:26 Jun 2012 08:23
Last Modified:26 Jun 2012 08:23

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