Add to ORKGThe linear discriminant function which is optimal for discriminating between normal alternatives is shown to be optimum for the class of elliptical normal mixtures. Some methods for evaluating the probabilities of correct classification of the two-group discrimination problem are discussed.Elliptical distributions elliptical normal mixtures minimum distance classification linear discriminant function success rates
[[abstract]]A classification rule based on the minimum Kolmogorov distance for classifying an indivi...
Various parametric and nonparametric approaches to multiple discriminant analysis attempt to discrim...
Various parametric and nonparametric approaches to multiple discriminant analysis attempt to discrim...
Two optimal characteristic properties of the normal distribution are shown: (a) Of all the SNM (sphe...
AbstractTwo optimal characteristic properties of the normal distribution are shown: (a) Of all the S...
A class of discriminant rules which includes the Fisher’s linear discriminant function and the likel...
Optimal classification rules based on linear functions which maximize the area under the relative op...
A simulation study is carried out to compare three distance-based classifiers for their misclassific...
A simulation study is carried out to compare three distance-based classifiers for their misclassific...
The problem of discriminating between two n-variate normal populations with known but unequal means ...
For a given data set the problem of selecting either log-normal or gamma distribu-tion with unknown ...
Building on probabilistic models for interval-valued variables, parametric classification rules, bas...
AbstractThe current study provides a simple algorithm for finding the optimal ROC curve for a linear...
Optimal classification rules based on linear functions which maximize the area under the relative o...
A comparison of the error probabilities for various discriminating rules is performed in the two pop...
[[abstract]]A classification rule based on the minimum Kolmogorov distance for classifying an indivi...
Various parametric and nonparametric approaches to multiple discriminant analysis attempt to discrim...
Various parametric and nonparametric approaches to multiple discriminant analysis attempt to discrim...
Two optimal characteristic properties of the normal distribution are shown: (a) Of all the SNM (sphe...
AbstractTwo optimal characteristic properties of the normal distribution are shown: (a) Of all the S...
A class of discriminant rules which includes the Fisher’s linear discriminant function and the likel...
Optimal classification rules based on linear functions which maximize the area under the relative op...
A simulation study is carried out to compare three distance-based classifiers for their misclassific...
A simulation study is carried out to compare three distance-based classifiers for their misclassific...
The problem of discriminating between two n-variate normal populations with known but unequal means ...
For a given data set the problem of selecting either log-normal or gamma distribu-tion with unknown ...
Building on probabilistic models for interval-valued variables, parametric classification rules, bas...
AbstractThe current study provides a simple algorithm for finding the optimal ROC curve for a linear...
Optimal classification rules based on linear functions which maximize the area under the relative o...
A comparison of the error probabilities for various discriminating rules is performed in the two pop...
[[abstract]]A classification rule based on the minimum Kolmogorov distance for classifying an indivi...
Various parametric and nonparametric approaches to multiple discriminant analysis attempt to discrim...
Various parametric and nonparametric approaches to multiple discriminant analysis attempt to discrim...