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...

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Main Authors: Warisa Thangjai,, Sa-Aat Niwitpong,, Suparat Niwitpong,
Format: Article
Language:English
Published: Penerbit Universiti Kebangsaan Malaysia 2021
Online Access:http://journalarticle.ukm.my/16410/1/25.pdf
http://journalarticle.ukm.my/16410/
https://www.ukm.my/jsm/malay_journals/jilid50bil1_2021/KandunganJilid50Bil1_2021.html
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spelling my-ukm.journal.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
institution Universiti Kebangsaan Malaysia
building Tun Sri Lanang Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Kebangsaan Malaysia
content_source UKM Journal Article Repository
url_provider http://journalarticle.ukm.my/
language English
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.
format Article
author Warisa Thangjai,
Sa-Aat Niwitpong,
Suparat Niwitpong,
spellingShingle Warisa Thangjai,
Sa-Aat Niwitpong,
Suparat Niwitpong,
A Bayesian approach for estimation of coefficients of variation of normal distributions
author_facet Warisa Thangjai,
Sa-Aat Niwitpong,
Suparat Niwitpong,
author_sort Warisa Thangjai,
title 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_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_sort bayesian approach for estimation of coefficients of variation of normal distributions
publisher Penerbit Universiti Kebangsaan Malaysia
publishDate 2021
url http://journalarticle.ukm.my/16410/1/25.pdf
http://journalarticle.ukm.my/16410/
https://www.ukm.my/jsm/malay_journals/jilid50bil1_2021/KandunganJilid50Bil1_2021.html
_version_ 1696977215634276352
score 13.160551