[[abstract]]A classification rule based on the minimum Kolmogorov distance for classifying an individual into one of two univariate populations is proposed. Some almost sure convergence theorems are derived and then utilized to study the limiting distributions of the conditional probabilities of correct classification. It is shown that the asymptotic distributions of the conditional probabilities of correct classification are normal under some mild conditions.[[notice]]補正完畢[[journaltype]]國外[[incitationindex]]SC
In this paper, we study a two-category classification problem. We indicate the cate-gories by labels...
We consider a situation where two sample sets of independent real valued observations are obtained f...
The deficiency distance between a multinomial and a multivariate normal experiment is bounded under ...
The distributions of the conditional error rate and risk associated with Anderson's classification s...
Decision-theoretic and classical formulations of the ranking problems in a nonparametric setup are c...
Linear procedures for classifying an observation as coming from one of two multivariate normal distr...
AbstractIn this paper an optimum procedure, based on the maximum-likehood criterion, for classificat...
The linear discriminant function which is optimal for discriminating between normal alternatives is ...
Consider the multiclassification (discrimination) problem with known prior probabilities and a multi...
In this paper, we study a two-category classification problem. We indicate the categories by labels ...
For a given data set the problem of selecting either log-normal or gamma distribu-tion with unknown ...
This report deals with the problem of learning to classify observations from a mixture of random var...
Consider the multiclassification (discrimination) problem with known prior probabilities and a multi...
The problem of discriminating between two n-variate normal populations with known but unequal means ...
We introduce an approximate minimum Kolmogorov distance density estimate [InlineMediaObject not avai...
In this paper, we study a two-category classification problem. We indicate the cate-gories by labels...
We consider a situation where two sample sets of independent real valued observations are obtained f...
The deficiency distance between a multinomial and a multivariate normal experiment is bounded under ...
The distributions of the conditional error rate and risk associated with Anderson's classification s...
Decision-theoretic and classical formulations of the ranking problems in a nonparametric setup are c...
Linear procedures for classifying an observation as coming from one of two multivariate normal distr...
AbstractIn this paper an optimum procedure, based on the maximum-likehood criterion, for classificat...
The linear discriminant function which is optimal for discriminating between normal alternatives is ...
Consider the multiclassification (discrimination) problem with known prior probabilities and a multi...
In this paper, we study a two-category classification problem. We indicate the categories by labels ...
For a given data set the problem of selecting either log-normal or gamma distribu-tion with unknown ...
This report deals with the problem of learning to classify observations from a mixture of random var...
Consider the multiclassification (discrimination) problem with known prior probabilities and a multi...
The problem of discriminating between two n-variate normal populations with known but unequal means ...
We introduce an approximate minimum Kolmogorov distance density estimate [InlineMediaObject not avai...
In this paper, we study a two-category classification problem. We indicate the cate-gories by labels...
We consider a situation where two sample sets of independent real valued observations are obtained f...
The deficiency distance between a multinomial and a multivariate normal experiment is bounded under ...