The performance of mutual information for mixture of bivariate normal disatributions based on robust kernel estimation.
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...
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English English |
| Published: |
Hikari Ltd
2010
|
| Subjects: | |
| Online Access: | http://psasir.upm.edu.my/id/eprint/17263/ http://psasir.upm.edu.my/id/eprint/17263/1/The%20performance%20of%20mutual%20information%20for%20mixture%20of%20bivariate%20normal%20disatributions%20based%20on%20robust%20kernel%20estimation.pdf |
| _version_ | 1848843193352716288 |
|---|---|
| author | Dadkhah, Kourosh Midi, Habshah |
| author_facet | Dadkhah, Kourosh Midi, Habshah |
| author_sort | Dadkhah, Kourosh |
| building | UPM Institutional Repository |
| collection | Online Access |
| description | 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. |
| first_indexed | 2025-11-15T08:11:08Z |
| format | Article |
| id | upm-17263 |
| institution | Universiti Putra Malaysia |
| institution_category | Local University |
| language | English English |
| last_indexed | 2025-11-15T08:11:08Z |
| publishDate | 2010 |
| publisher | Hikari Ltd |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | upm-172632015-11-11T07:18:52Z http://psasir.upm.edu.my/id/eprint/17263/ The performance of mutual information for mixture of bivariate normal disatributions based on robust kernel estimation. Dadkhah, Kourosh Midi, Habshah 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. Hikari Ltd 2010 Article PeerReviewed application/pdf en http://psasir.upm.edu.my/id/eprint/17263/1/The%20performance%20of%20mutual%20information%20for%20mixture%20of%20bivariate%20normal%20disatributions%20based%20on%20robust%20kernel%20estimation.pdf 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 Robust statistics. Information theory. Probabilities. English |
| spellingShingle | Robust statistics. Information theory. Probabilities. Dadkhah, Kourosh Midi, Habshah The performance of mutual information for mixture of bivariate normal disatributions based on robust kernel estimation. |
| title | The performance of mutual information for mixture of bivariate normal disatributions based on robust kernel estimation. |
| title_full | The performance of mutual information for mixture of bivariate normal disatributions based on robust kernel estimation. |
| title_fullStr | The performance of mutual information for mixture of bivariate normal disatributions based on robust kernel estimation. |
| title_full_unstemmed | The performance of mutual information for mixture of bivariate normal disatributions based on robust kernel estimation. |
| title_short | The performance of mutual information for mixture of bivariate normal disatributions based on robust kernel estimation. |
| title_sort | performance of mutual information for mixture of bivariate normal disatributions based on robust kernel estimation. |
| topic | Robust statistics. Information theory. Probabilities. |
| url | http://psasir.upm.edu.my/id/eprint/17263/ http://psasir.upm.edu.my/id/eprint/17263/1/The%20performance%20of%20mutual%20information%20for%20mixture%20of%20bivariate%20normal%20disatributions%20based%20on%20robust%20kernel%20estimation.pdf |