A Bayesian approach for estimation of coefficients of variation of normal distributions
The coefficient of variation is widely used as a measure of data precision. Confidence intervals for a single coefficient of variation when the data follow a normal distribution that is symmetrical and the difference between the coefficients of variation of two normal populations are considered...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Penerbit Universiti Kebangsaan Malaysia
2021
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| Online Access: | http://journalarticle.ukm.my/16410/ http://journalarticle.ukm.my/16410/1/25.pdf |
| _version_ | 1848814045082157056 |
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| author | Warisa Thangjai, Sa-Aat Niwitpong, Suparat Niwitpong, |
| author_facet | Warisa Thangjai, Sa-Aat Niwitpong, Suparat Niwitpong, |
| author_sort | Warisa Thangjai, |
| building | UKM Institutional Repository |
| collection | Online Access |
| description | The coefficient of variation is widely used as a measure of data precision. Confidence intervals for a single coefficient
of variation when the data follow a normal distribution that is symmetrical and the difference between the coefficients
of variation of two normal populations are considered in this paper. First, the confidence intervals for the coefficient
of variation of a normal distribution are obtained with adjusted generalized confidence interval (adjusted GCI),
computational, Bayesian, and two adjusted Bayesian approaches. These approaches are compared with existing ones
comprising two approximately unbiased estimators, the method of variance estimates recovery (MOVER) and generalized
confidence interval (GCI). Second, the confidence intervals for the difference between the coefficients of variation of
two normal distributions are proposed using the same approaches, the performances of which are then compared with
the existing approaches. The highest posterior density interval was used to estimate the Bayesian confidence interval.
Monte Carlo simulation was used to assess the performance of the confidence intervals. The results of the simulation
studies demonstrate that the Bayesian and two adjusted Bayesian approaches were more accurate and better than the
others in terms of coverage probabilities and average lengths in both scenarios. Finally, the performances of all of the
approaches for both scenarios are illustrated via an empirical study with two real-data examples. |
| first_indexed | 2025-11-15T00:27:50Z |
| format | Article |
| id | oai:generic.eprints.org:16410 |
| institution | Universiti Kebangasaan Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T00:27:50Z |
| publishDate | 2021 |
| publisher | Penerbit Universiti Kebangsaan Malaysia |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | oai:generic.eprints.org:164102021-04-13T04:30:09Z http://journalarticle.ukm.my/16410/ A Bayesian approach for estimation of coefficients of variation of normal distributions Warisa Thangjai, Sa-Aat Niwitpong, Suparat Niwitpong, The coefficient of variation is widely used as a measure of data precision. Confidence intervals for a single coefficient of variation when the data follow a normal distribution that is symmetrical and the difference between the coefficients of variation of two normal populations are considered in this paper. First, the confidence intervals for the coefficient of variation of a normal distribution are obtained with adjusted generalized confidence interval (adjusted GCI), computational, Bayesian, and two adjusted Bayesian approaches. These approaches are compared with existing ones comprising two approximately unbiased estimators, the method of variance estimates recovery (MOVER) and generalized confidence interval (GCI). Second, the confidence intervals for the difference between the coefficients of variation of two normal distributions are proposed using the same approaches, the performances of which are then compared with the existing approaches. The highest posterior density interval was used to estimate the Bayesian confidence interval. Monte Carlo simulation was used to assess the performance of the confidence intervals. The results of the simulation studies demonstrate that the Bayesian and two adjusted Bayesian approaches were more accurate and better than the others in terms of coverage probabilities and average lengths in both scenarios. Finally, the performances of all of the approaches for both scenarios are illustrated via an empirical study with two real-data examples. Penerbit Universiti Kebangsaan Malaysia 2021-01 Article PeerReviewed application/pdf en http://journalarticle.ukm.my/16410/1/25.pdf Warisa Thangjai, and Sa-Aat Niwitpong, and Suparat Niwitpong, (2021) A Bayesian approach for estimation of coefficients of variation of normal distributions. Sains Malaysiana, 50 (1). pp. 261-278. ISSN 0126-6039 https://www.ukm.my/jsm/malay_journals/jilid50bil1_2021/KandunganJilid50Bil1_2021.html |
| spellingShingle | Warisa Thangjai, Sa-Aat Niwitpong, Suparat Niwitpong, A Bayesian approach for estimation of coefficients of variation of normal distributions |
| title | A Bayesian approach for estimation of coefficients of variation of normal distributions |
| title_full | A Bayesian approach for estimation of coefficients of variation of normal distributions |
| title_fullStr | A Bayesian approach for estimation of coefficients of variation of normal distributions |
| title_full_unstemmed | A Bayesian approach for estimation of coefficients of variation of normal distributions |
| title_short | A Bayesian approach for estimation of coefficients of variation of normal distributions |
| title_sort | bayesian approach for estimation of coefficients of variation of normal distributions |
| url | http://journalarticle.ukm.my/16410/ http://journalarticle.ukm.my/16410/ http://journalarticle.ukm.my/16410/1/25.pdf |