Dimension Reduction with Linear Discrimant Functions Based on an Odds Ratio Parameterization
The association of two random elements with positive joint probability density function is given by an odds ratio function. The covariance is an adequate description only in the case of two jointly Gaussian variables. The impact of the association structure on the set-up and solution of problems of...
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Veröffentlicht in: | International statistical review 2003-12, Vol.71 (3), p.629-666 |
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description | The association of two random elements with positive joint probability density function is given by an odds ratio function. The covariance is an adequate description only in the case of two jointly Gaussian variables. The impact of the association structure on the set-up and solution of problems of linear discrimination is investigated, and the results are related to standard techniques of multivariate analysis, particularly to canonical correlation analysis, analysis of contingency tables, discriminant analysis and multidimensional scaling. /// L'association entre deux éléments aléatoires ayant une densité conjointe positive est donnée par l'odds ratio fonction. La covariance n'est une description adéquate que dans le cas gaussien. L'impact de ce résultat en termes de réduction de la dimension et en terme de discrimination est examinée et les résultats sont rapportés aux techniques traditionelles de l'analyse discriminante et du scaling multidimensional. |
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The covariance is an adequate description only in the case of two jointly Gaussian variables. The impact of the association structure on the set-up and solution of problems of linear discrimination is investigated, and the results are related to standard techniques of multivariate analysis, particularly to canonical correlation analysis, analysis of contingency tables, discriminant analysis and multidimensional scaling. /// L'association entre deux éléments aléatoires ayant une densité conjointe positive est donnée par l'odds ratio fonction. La covariance n'est une description adéquate que dans le cas gaussien. L'impact de ce résultat en termes de réduction de la dimension et en terme de discrimination est examinée et les résultats sont rapportés aux techniques traditionelles de l'analyse discriminante et du scaling multidimensional.</abstract><pub>International Statistical Institute</pub></addata></record> |
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subjects | Covariance Covariance matrices Discriminants Eigenvectors Gaussian distributions Linear discriminant analysis Mathematical vectors Modeling Probabilities Regression analysis |
title | Dimension Reduction with Linear Discrimant Functions Based on an Odds Ratio Parameterization |
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