Add to ORKGAn optimal measure of performance is the one that lead to maximization of average error rate or probability of misclassification. This paper aimed to compare between the maximum likelihood rule and logistic discriminant analysis in the classification of mixture of discrete and continuous variables. The efficiency of the methods was tested using simulated and real dataset. The result obtained showed that the maximum likelihood rule performed better than the logistic discriminant analyses, in maximizing the average error rate in both experiment conducted. Keyword: Maximum likelihood rule, Logistic discriminants, error rate, Likelihood ratio, Discriminant analysis
This study provides a comprehensive review of the literature pertaining to the problem of classifica...
peer reviewedA simulation study has been used to evaluate the minimal error rate of three affectatio...
Two of the most widely used statistical methods for analyzing categorical outcome variables are line...
The best classification rule is the one that leads to the smallest probability of misclassification ...
This paper reports research into maximum likelihood parameter estimation for classification of data ...
In this paper we show the results of a comparison simulation study for three classification techniqu...
M (Statistics), North-West University, Mafikeng CampusThis study compared the performance of two of ...
This paper proposes a Bootstrap algorithm for linear discriminant analysis. The apparent error rate ...
This paper is a survey study on applications of boot- strap methods for estimating the probability o...
This paper is concerned with the problem of estimating the error rate in two-group discriminant anal...
This paper is concerned with the problem of estimating the error rate in two-group discriminant anal...
In discriminant analysis involving continuous and categorical variables, the simplest and convention...
This paper is a survey study on applications of boot- strap methods for estimating the probability o...
AbstractMonte Carlo estimates have been obtained for the unconditional probability of misclassificat...
A review is given on existing work and result of the performance of some discriminant analysis proce...
This study provides a comprehensive review of the literature pertaining to the problem of classifica...
peer reviewedA simulation study has been used to evaluate the minimal error rate of three affectatio...
Two of the most widely used statistical methods for analyzing categorical outcome variables are line...
The best classification rule is the one that leads to the smallest probability of misclassification ...
This paper reports research into maximum likelihood parameter estimation for classification of data ...
In this paper we show the results of a comparison simulation study for three classification techniqu...
M (Statistics), North-West University, Mafikeng CampusThis study compared the performance of two of ...
This paper proposes a Bootstrap algorithm for linear discriminant analysis. The apparent error rate ...
This paper is a survey study on applications of boot- strap methods for estimating the probability o...
This paper is concerned with the problem of estimating the error rate in two-group discriminant anal...
This paper is concerned with the problem of estimating the error rate in two-group discriminant anal...
In discriminant analysis involving continuous and categorical variables, the simplest and convention...
This paper is a survey study on applications of boot- strap methods for estimating the probability o...
AbstractMonte Carlo estimates have been obtained for the unconditional probability of misclassificat...
A review is given on existing work and result of the performance of some discriminant analysis proce...
This study provides a comprehensive review of the literature pertaining to the problem of classifica...
peer reviewedA simulation study has been used to evaluate the minimal error rate of three affectatio...
Two of the most widely used statistical methods for analyzing categorical outcome variables are line...