Bayesian Confidence Intervals for Coefficients of Variation of PM10 Dispersion
Herein, we propose the Bayesian approach for constructing the confidence intervals for both the coefficient of variation of a log-normal distribution and the difference between the coefficients of variation of two log-normal distributions. For the first case, the Bayesian approach was compared with...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Ital Publication
2021-04-01
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| Series: | Emerging Science Journal |
| Subjects: | |
| Online Access: | https://www.ijournalse.org/index.php/ESJ/article/view/447 |
| _version_ | 1828809208135417856 |
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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 |
| collection | DOAJ |
| description | Herein, we propose the Bayesian approach for constructing the confidence intervals for both the coefficient of variation of a log-normal distribution and the difference between the coefficients of variation of two log-normal distributions. For the first case, the Bayesian approach was compared with large-sample, Chi-squared, and approximate fiducial approaches via Monte Carlo simulation. For the second case, the Bayesian approach was compared with the method of variance estimates recovery (MOVER), modified MOVER, and approximate fiducial approaches using Monte Carlo simulation. The results show that the Bayesian approach provided the best approach for constructing the confidence intervals for both the coefficient of variation of a log-normal distribution and the difference between the coefficients of variation of two log-normal distributions. To illustrate the performances of the confidence limit construction approaches with real data, they were applied to analyze real PM10 datasets from the Nan and Chiang Mai provinces in Thailand, the results of which are in agreement with the simulation results.
Doi: 10.28991/esj-2021-01264
Full Text: PDF |
| first_indexed | 2024-12-12T08:53:09Z |
| format | Article |
| id | doaj.art-f1f91aee3af54edb8a68803e64afb6de |
| institution | Directory Open Access Journal |
| issn | 2610-9182 |
| language | English |
| last_indexed | 2024-12-12T08:53:09Z |
| publishDate | 2021-04-01 |
| publisher | Ital Publication |
| record_format | Article |
| series | Emerging Science Journal |
| spelling | doaj.art-f1f91aee3af54edb8a68803e64afb6de2022-12-22T00:30:08ZengItal PublicationEmerging Science Journal2610-91822021-04-015213915410.28991/esj-2021-01264173Bayesian Confidence Intervals for Coefficients of Variation of PM10 DispersionWarisa Thangjai0Sa-Aat Niwitpong1Suparat Niwitpong2Department of Statistics, Faculty of Science, Ramkhamhaeng University, Bangkok, 10240Department of Applied Statistics, Faculty of Applied Science, King Mongkut's University of Technology North Bangkok, Bangkok, 10800Department of Applied Statistics, Faculty of Applied Science, King Mongkut's University of Technology North Bangkok, Bangkok, 10800Herein, we propose the Bayesian approach for constructing the confidence intervals for both the coefficient of variation of a log-normal distribution and the difference between the coefficients of variation of two log-normal distributions. For the first case, the Bayesian approach was compared with large-sample, Chi-squared, and approximate fiducial approaches via Monte Carlo simulation. For the second case, the Bayesian approach was compared with the method of variance estimates recovery (MOVER), modified MOVER, and approximate fiducial approaches using Monte Carlo simulation. The results show that the Bayesian approach provided the best approach for constructing the confidence intervals for both the coefficient of variation of a log-normal distribution and the difference between the coefficients of variation of two log-normal distributions. To illustrate the performances of the confidence limit construction approaches with real data, they were applied to analyze real PM10 datasets from the Nan and Chiang Mai provinces in Thailand, the results of which are in agreement with the simulation results. Doi: 10.28991/esj-2021-01264 Full Text: PDFhttps://www.ijournalse.org/index.php/ESJ/article/view/447bayesian approachcoefficient of variationdifferencelog-normal distributionmonte carlo simulation. |
| spellingShingle | Warisa Thangjai Sa-Aat Niwitpong Suparat Niwitpong Bayesian Confidence Intervals for Coefficients of Variation of PM10 Dispersion Emerging Science Journal bayesian approach coefficient of variation difference log-normal distribution monte carlo simulation. |
| title | Bayesian Confidence Intervals for Coefficients of Variation of PM10 Dispersion |
| title_full | Bayesian Confidence Intervals for Coefficients of Variation of PM10 Dispersion |
| title_fullStr | Bayesian Confidence Intervals for Coefficients of Variation of PM10 Dispersion |
| title_full_unstemmed | Bayesian Confidence Intervals for Coefficients of Variation of PM10 Dispersion |
| title_short | Bayesian Confidence Intervals for Coefficients of Variation of PM10 Dispersion |
| title_sort | bayesian confidence intervals for coefficients of variation of pm10 dispersion |
| topic | bayesian approach coefficient of variation difference log-normal distribution monte carlo simulation. |
| url | https://www.ijournalse.org/index.php/ESJ/article/view/447 |
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