Approximation of misclassification probabilities in linear discriminant analysis based on repeated measurements
The classification of observations based on repeated measurements performed on the same subject over a given period of time or under different conditions is a common procedure in many disciplines such as medicine, psychology and environmental studies. In this article repeated measurements follow an...
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| Published in | Communications in statistics. Theory and methods Vol. 52; no. 23; pp. 8388 - 8407 |
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| Main Authors | , |
| Format | Journal Article |
| Language | English |
| Published |
Philadelphia
Taylor & Francis
02.12.2023
Taylor & Francis Ltd |
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| ISSN | 0361-0926 1532-415X |
| DOI | 10.1080/03610926.2022.2062605 |
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| Abstract | The classification of observations based on repeated measurements performed on the same subject over a given period of time or under different conditions is a common procedure in many disciplines such as medicine, psychology and environmental studies. In this article repeated measurements follow an extended growth curve model and are classified using linear discriminant analysis. The aim of this article is to propose approximation for the misclassification probabilities in the linear discriminant function when the population means follow an extended growth curve structure. Using specific statistic relations we derive the approximation of misclassification probabilities for known and unknown covariance matrices. Finally, we perform a Monte Carlo simulation study to assess the accuracy of the developed results. |
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| AbstractList | The classification of observations based on repeated measurements performed on the same subject over a given period of time or under different conditions is a common procedure in many disciplines such as medicine, psychology and environmental studies. In this article repeated measurements follow an extended growth curve model and are classified using linear discriminant analysis. The aim of this article is to propose approximation for the misclassification probabilities in the linear discriminant function when the population means follow an extended growth curve structure. Using specific statistic relations we derive the approximation of misclassification probabilities for known and unknown covariance matrices. Finally, we perform a Monte Carlo simulation study to assess the accuracy of the developed results. |
| Author | Chuma, Furaha Ngailo, Edward Kanuti |
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| SubjectTerms | Approximation Covariance matrix Discriminant analysis Environmental studies extended growth curve model growth curve model linear discriminant function Mathematical analysis Monte Carlo simulation probability of misclassification |
| Title | Approximation of misclassification probabilities in linear discriminant analysis based on repeated measurements |
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