COVRATIO statistic as a discrimination method for multivariate normal distribution

The COVRATIO statistic has been used to identify the presence of outlier in data, which is based on deletion approach, where the determinant of covariance matrix for the full dataset excludes i-th row. This study proposes a novel discrimination method for the multivariate normal (MVN) distribution u...

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Main Authors: Norli Anida Abdullah, Yong Zulina Zubairi, Afera Mohamad Apandi, Mohd Iqbal Shamsudheen
Format: Article
Language:English
Published: Penerbit Universiti Kebangsaan Malaysia 2021
Online Access:http://journalarticle.ukm.my/17567/1/21.pdf
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author Norli Anida Abdullah,
Yong Zulina Zubairi,
Afera Mohamad Apandi,
Mohd Iqbal Shamsudheen,
author_facet Norli Anida Abdullah,
Yong Zulina Zubairi,
Afera Mohamad Apandi,
Mohd Iqbal Shamsudheen,
author_sort Norli Anida Abdullah,
collection UKM
description The COVRATIO statistic has been used to identify the presence of outlier in data, which is based on deletion approach, where the determinant of covariance matrix for the full dataset excludes i-th row. This study proposes a novel discrimination method for the multivariate normal (MVN) distribution using the idea of COVRATIO statistic, denoted as COVRATIO(+i). The linear discrimination function (LDF) for MVN distribution will be compared to the COVRATIO(+i) statistic. Simulation results showed that the COVRATIO(+i) as discrimination method performs better than the LDF with lower misclassification probabilities in all cases considered. The interest in the discrimination method arose in connection with the study of an application to discriminate the shape of the human maxillary dental arches, thus COVRATIO(+i) statistic may be considered as an alternative.
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spelling ukm.eprints-175672021-11-15T03:48:39Z http://journalarticle.ukm.my/17567/ COVRATIO statistic as a discrimination method for multivariate normal distribution Norli Anida Abdullah, Yong Zulina Zubairi, Afera Mohamad Apandi, Mohd Iqbal Shamsudheen, The COVRATIO statistic has been used to identify the presence of outlier in data, which is based on deletion approach, where the determinant of covariance matrix for the full dataset excludes i-th row. This study proposes a novel discrimination method for the multivariate normal (MVN) distribution using the idea of COVRATIO statistic, denoted as COVRATIO(+i). The linear discrimination function (LDF) for MVN distribution will be compared to the COVRATIO(+i) statistic. Simulation results showed that the COVRATIO(+i) as discrimination method performs better than the LDF with lower misclassification probabilities in all cases considered. The interest in the discrimination method arose in connection with the study of an application to discriminate the shape of the human maxillary dental arches, thus COVRATIO(+i) statistic may be considered as an alternative. Penerbit Universiti Kebangsaan Malaysia 2021-07 Article PeerReviewed application/pdf en http://journalarticle.ukm.my/17567/1/21.pdf Norli Anida Abdullah, and Yong Zulina Zubairi, and Afera Mohamad Apandi, and Mohd Iqbal Shamsudheen, (2021) COVRATIO statistic as a discrimination method for multivariate normal distribution. Sains Malaysiana, 50 (7). pp. 2079-2084. ISSN 0126-6039 https://www.ukm.my/jsm/malay_journals/jilid50bil7_2021/KandunganJilid50Bil7_2021.html
spellingShingle Norli Anida Abdullah,
Yong Zulina Zubairi,
Afera Mohamad Apandi,
Mohd Iqbal Shamsudheen,
COVRATIO statistic as a discrimination method for multivariate normal distribution
title COVRATIO statistic as a discrimination method for multivariate normal distribution
title_full COVRATIO statistic as a discrimination method for multivariate normal distribution
title_fullStr COVRATIO statistic as a discrimination method for multivariate normal distribution
title_full_unstemmed COVRATIO statistic as a discrimination method for multivariate normal distribution
title_short COVRATIO statistic as a discrimination method for multivariate normal distribution
title_sort covratio statistic as a discrimination method for multivariate normal distribution
url http://journalarticle.ukm.my/17567/1/21.pdf
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