In an effort to further explore a specific aspect of rule-space theory, identification of students with respect to their error rules, three major questions were raised: (a) How do the discriminant-function approach (DA) and the k-nearest-neighbor approach (KNN) perform for the classification of each bivariate-normally- and non-bivariate-normally-distributed item-response patterns in rule space? (b) Is the use of rule-space model better than not using it for classifications? (c) What is the minimum pairwise distance for a 90% correct classification?A Monte Carlo study was executed. Two independent and identical sample sets of item-response patterns were simulated. For each sample, two hundred item-response patterns in each rule group, for a ...
The following thesis studies both parametric and non parametric approaches to classification. Among ...
Discriminant analysis predicts or classifies observations or subjects into mutually exclusive groups...
This study had four main objectives and two minor objectives: (1) To compare the Type I Error rates ...
In an effort to further explore a specific aspect of rule-space theory, identification of students w...
The main goal of the rule space method is to classify examinees into the closest ideal attribute mas...
AbstractA simulation study was performed to investigate the sensitivity of the k-nearest neighbor (N...
The study was designed to examine the various statistical techniques for computing the Discriminatin...
This thesis concerns the development and mathematical analysis of statistical procedures for classi...
This study focuses on supervised learning, an aspect of statistical learning. The supervised learnin...
The best classification rule is the one that leads to the smallest probability of misclassification ...
In the pattern recognition literature, Huang and Suen introduced the “multinomial” rule for fusion o...
Both methods, Rule Space Method (RSM) and Neural Network Model (NNM) are techniques of statistical p...
The statistical classification of N individuals into G mutually exclusive groups when the actual gro...
Abstract: Both methods, Rule Space Method (RSM) and Neural Network Model (NNM) are techniques of sta...
The effect of person misfit to an item response theory (IRT) model on a mastery/nonmastery decision ...
The following thesis studies both parametric and non parametric approaches to classification. Among ...
Discriminant analysis predicts or classifies observations or subjects into mutually exclusive groups...
This study had four main objectives and two minor objectives: (1) To compare the Type I Error rates ...
In an effort to further explore a specific aspect of rule-space theory, identification of students w...
The main goal of the rule space method is to classify examinees into the closest ideal attribute mas...
AbstractA simulation study was performed to investigate the sensitivity of the k-nearest neighbor (N...
The study was designed to examine the various statistical techniques for computing the Discriminatin...
This thesis concerns the development and mathematical analysis of statistical procedures for classi...
This study focuses on supervised learning, an aspect of statistical learning. The supervised learnin...
The best classification rule is the one that leads to the smallest probability of misclassification ...
In the pattern recognition literature, Huang and Suen introduced the “multinomial” rule for fusion o...
Both methods, Rule Space Method (RSM) and Neural Network Model (NNM) are techniques of statistical p...
The statistical classification of N individuals into G mutually exclusive groups when the actual gro...
Abstract: Both methods, Rule Space Method (RSM) and Neural Network Model (NNM) are techniques of sta...
The effect of person misfit to an item response theory (IRT) model on a mastery/nonmastery decision ...
The following thesis studies both parametric and non parametric approaches to classification. Among ...
Discriminant analysis predicts or classifies observations or subjects into mutually exclusive groups...
This study had four main objectives and two minor objectives: (1) To compare the Type I Error rates ...