Comparing particulate matter dispersion in Thailand using the Bayesian Confidence Intervals for ratio of coefficients of variation

Recently, harmful levels of air pollution have been detected in many provinces of Thailand. Particulate matter (PM) contains microscopic solids or liquid droplets that are so small that they can be inhaled and cause serious health problems. A high dispersion of PM is measured by a coefficient of var...
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Autor*in:	Warisa Thangjai [verfasserIn] Suparat Niwitpong [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2020

Rechteinformationen:	Open Access Namensnennung 4.0 International ; CC BY 4.0

Schlagwörter:	Bayesian approach coefficient of variation confidence interval log-normal distribution ratio

Übergeordnetes Werk:	Enthalten in: Statistics in transition - Warszawa : GUS, 2000, 21(2020), 5 vom: Dez., Seite 41-60
Übergeordnetes Werk:	volume:21 ; year:2020 ; number:5 ; month:12 ; pages:41-60

Links:	Link aufrufen Link aufrufen Link aufrufen

DOI / URN:	10.21307/stattrans-2020-054

Katalog-ID:	1776200543

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spelling	10.21307/stattrans-2020-054 doi 10419/236804 hdl (DE-627)1776200543 (DE-599)KXP1776200543 DE-627 ger DE-627 rda eng Warisa Thangjai verfasserin (DE-588)1154406997 (DE-627)1015696635 (DE-576)500922179 aut Comparing particulate matter dispersion in Thailand using the Bayesian Confidence Intervals for ratio of coefficients of variation Warisa Thangjai, Suparat Niwitpong 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier DE-206 Open Access Controlled Vocabulary for Access Rights http://purl.org/coar/access_right/c_abf2 Recently, harmful levels of air pollution have been detected in many provinces of Thailand. Particulate matter (PM) contains microscopic solids or liquid droplets that are so small that they can be inhaled and cause serious health problems. A high dispersion of PM is measured by a coefficient of variation of log-normal distribution. Since the log-normal distribution is often used to analyse environmental data such as hazardous dust particle levels and daily rainfall data. These data focus the statistical inference on the coefficient of variation. In this paper, we develop confidence interval estimation for the ratio of coefficients of variation of two log-normal distributions constructed using the Bayesian approach. These confidence intervals were then compared with the existing approaches: method of variance estimates recovery (MOVER), modified MOVER, and approximate fiducial approaches using their coverage probabilities and average lengths via Monte Carlo simulation. The simulation results show that the Bayesian confidence interval performed better than the others in terms of coverage probability and average length. The proposed approach and the existing approaches are illustrated using examples from data set PM10 level and PM2.5 level in the northern Thailand. DE-206 Namensnennung 4.0 International CC BY 4.0 cc https://creativecommons.org/licenses/by/4.0/ Bayesian approach (dpeaa)DE-206 coefficient of variation (dpeaa)DE-206 confidence interval (dpeaa)DE-206 log-normal distribution (dpeaa)DE-206 ratio (dpeaa)DE-206 Suparat Niwitpong verfasserin (DE-588)1154407071 (DE-627)1015696724 (DE-576)500922136 aut Enthalten in Statistics in transition Warszawa : GUS, 2000 21(2020), 5 vom: Dez., Seite 41-60 Online-Ressource (DE-627)512298068 (DE-600)2235641-1 (DE-576)281309450 2450-0291 nnns volume:21 year:2020 number:5 month:12 pages:41-60 https://www.exeley.com/statistics_in_transition/pdf/10.21307/stattrans-2020-054 Verlag kostenfrei https://doi.org/10.21307/stattrans-2020-054 Resolving-System kostenfrei http://hdl.handle.net/10419/236804 Resolving-System kostenfrei GBV_USEFLAG_U GBV_ILN_26 ISIL_DE-206 SYSFLAG_1 GBV_KXP GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_138 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_374 GBV_ILN_602 GBV_ILN_647 GBV_ILN_702 GBV_ILN_2014 GBV_ILN_2863 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 GBV_ILN_2403 GBV_ILN_2403 ISIL_DE-LFER AR 21 2020 5 12 41-60 26 01 0206 3999125756 x1z 04-11-21 2403 01 DE-LFER 400155495X 00 --%%-- --%%-- n --%%-- l01 11-11-21 2403 01 DE-LFER https://doi.org/10.21307/stattrans-2020-054 2403 01 DE-LFER https://www.exeley.com/statistics_in_transition/pdf/10.21307/stattrans-2020-054
allfields_unstemmed	10.21307/stattrans-2020-054 doi 10419/236804 hdl (DE-627)1776200543 (DE-599)KXP1776200543 DE-627 ger DE-627 rda eng Warisa Thangjai verfasserin (DE-588)1154406997 (DE-627)1015696635 (DE-576)500922179 aut Comparing particulate matter dispersion in Thailand using the Bayesian Confidence Intervals for ratio of coefficients of variation Warisa Thangjai, Suparat Niwitpong 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier DE-206 Open Access Controlled Vocabulary for Access Rights http://purl.org/coar/access_right/c_abf2 Recently, harmful levels of air pollution have been detected in many provinces of Thailand. Particulate matter (PM) contains microscopic solids or liquid droplets that are so small that they can be inhaled and cause serious health problems. A high dispersion of PM is measured by a coefficient of variation of log-normal distribution. Since the log-normal distribution is often used to analyse environmental data such as hazardous dust particle levels and daily rainfall data. These data focus the statistical inference on the coefficient of variation. In this paper, we develop confidence interval estimation for the ratio of coefficients of variation of two log-normal distributions constructed using the Bayesian approach. These confidence intervals were then compared with the existing approaches: method of variance estimates recovery (MOVER), modified MOVER, and approximate fiducial approaches using their coverage probabilities and average lengths via Monte Carlo simulation. The simulation results show that the Bayesian confidence interval performed better than the others in terms of coverage probability and average length. The proposed approach and the existing approaches are illustrated using examples from data set PM10 level and PM2.5 level in the northern Thailand. DE-206 Namensnennung 4.0 International CC BY 4.0 cc https://creativecommons.org/licenses/by/4.0/ Bayesian approach (dpeaa)DE-206 coefficient of variation (dpeaa)DE-206 confidence interval (dpeaa)DE-206 log-normal distribution (dpeaa)DE-206 ratio (dpeaa)DE-206 Suparat Niwitpong verfasserin (DE-588)1154407071 (DE-627)1015696724 (DE-576)500922136 aut Enthalten in Statistics in transition Warszawa : GUS, 2000 21(2020), 5 vom: Dez., Seite 41-60 Online-Ressource (DE-627)512298068 (DE-600)2235641-1 (DE-576)281309450 2450-0291 nnns volume:21 year:2020 number:5 month:12 pages:41-60 https://www.exeley.com/statistics_in_transition/pdf/10.21307/stattrans-2020-054 Verlag kostenfrei https://doi.org/10.21307/stattrans-2020-054 Resolving-System kostenfrei http://hdl.handle.net/10419/236804 Resolving-System kostenfrei GBV_USEFLAG_U GBV_ILN_26 ISIL_DE-206 SYSFLAG_1 GBV_KXP GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_138 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_374 GBV_ILN_602 GBV_ILN_647 GBV_ILN_702 GBV_ILN_2014 GBV_ILN_2863 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 GBV_ILN_2403 GBV_ILN_2403 ISIL_DE-LFER AR 21 2020 5 12 41-60 26 01 0206 3999125756 x1z 04-11-21 2403 01 DE-LFER 400155495X 00 --%%-- --%%-- n --%%-- l01 11-11-21 2403 01 DE-LFER https://doi.org/10.21307/stattrans-2020-054 2403 01 DE-LFER https://www.exeley.com/statistics_in_transition/pdf/10.21307/stattrans-2020-054
allfieldsGer	10.21307/stattrans-2020-054 doi 10419/236804 hdl (DE-627)1776200543 (DE-599)KXP1776200543 DE-627 ger DE-627 rda eng Warisa Thangjai verfasserin (DE-588)1154406997 (DE-627)1015696635 (DE-576)500922179 aut Comparing particulate matter dispersion in Thailand using the Bayesian Confidence Intervals for ratio of coefficients of variation Warisa Thangjai, Suparat Niwitpong 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier DE-206 Open Access Controlled Vocabulary for Access Rights http://purl.org/coar/access_right/c_abf2 Recently, harmful levels of air pollution have been detected in many provinces of Thailand. Particulate matter (PM) contains microscopic solids or liquid droplets that are so small that they can be inhaled and cause serious health problems. A high dispersion of PM is measured by a coefficient of variation of log-normal distribution. Since the log-normal distribution is often used to analyse environmental data such as hazardous dust particle levels and daily rainfall data. These data focus the statistical inference on the coefficient of variation. In this paper, we develop confidence interval estimation for the ratio of coefficients of variation of two log-normal distributions constructed using the Bayesian approach. These confidence intervals were then compared with the existing approaches: method of variance estimates recovery (MOVER), modified MOVER, and approximate fiducial approaches using their coverage probabilities and average lengths via Monte Carlo simulation. The simulation results show that the Bayesian confidence interval performed better than the others in terms of coverage probability and average length. The proposed approach and the existing approaches are illustrated using examples from data set PM10 level and PM2.5 level in the northern Thailand. DE-206 Namensnennung 4.0 International CC BY 4.0 cc https://creativecommons.org/licenses/by/4.0/ Bayesian approach (dpeaa)DE-206 coefficient of variation (dpeaa)DE-206 confidence interval (dpeaa)DE-206 log-normal distribution (dpeaa)DE-206 ratio (dpeaa)DE-206 Suparat Niwitpong verfasserin (DE-588)1154407071 (DE-627)1015696724 (DE-576)500922136 aut Enthalten in Statistics in transition Warszawa : GUS, 2000 21(2020), 5 vom: Dez., Seite 41-60 Online-Ressource (DE-627)512298068 (DE-600)2235641-1 (DE-576)281309450 2450-0291 nnns volume:21 year:2020 number:5 month:12 pages:41-60 https://www.exeley.com/statistics_in_transition/pdf/10.21307/stattrans-2020-054 Verlag kostenfrei https://doi.org/10.21307/stattrans-2020-054 Resolving-System kostenfrei http://hdl.handle.net/10419/236804 Resolving-System kostenfrei GBV_USEFLAG_U GBV_ILN_26 ISIL_DE-206 SYSFLAG_1 GBV_KXP GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_138 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_374 GBV_ILN_602 GBV_ILN_647 GBV_ILN_702 GBV_ILN_2014 GBV_ILN_2863 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 GBV_ILN_2403 GBV_ILN_2403 ISIL_DE-LFER AR 21 2020 5 12 41-60 26 01 0206 3999125756 x1z 04-11-21 2403 01 DE-LFER 400155495X 00 --%%-- --%%-- n --%%-- l01 11-11-21 2403 01 DE-LFER https://doi.org/10.21307/stattrans-2020-054 2403 01 DE-LFER https://www.exeley.com/statistics_in_transition/pdf/10.21307/stattrans-2020-054
allfieldsSound	10.21307/stattrans-2020-054 doi 10419/236804 hdl (DE-627)1776200543 (DE-599)KXP1776200543 DE-627 ger DE-627 rda eng Warisa Thangjai verfasserin (DE-588)1154406997 (DE-627)1015696635 (DE-576)500922179 aut Comparing particulate matter dispersion in Thailand using the Bayesian Confidence Intervals for ratio of coefficients of variation Warisa Thangjai, Suparat Niwitpong 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier DE-206 Open Access Controlled Vocabulary for Access Rights http://purl.org/coar/access_right/c_abf2 Recently, harmful levels of air pollution have been detected in many provinces of Thailand. Particulate matter (PM) contains microscopic solids or liquid droplets that are so small that they can be inhaled and cause serious health problems. A high dispersion of PM is measured by a coefficient of variation of log-normal distribution. Since the log-normal distribution is often used to analyse environmental data such as hazardous dust particle levels and daily rainfall data. These data focus the statistical inference on the coefficient of variation. In this paper, we develop confidence interval estimation for the ratio of coefficients of variation of two log-normal distributions constructed using the Bayesian approach. These confidence intervals were then compared with the existing approaches: method of variance estimates recovery (MOVER), modified MOVER, and approximate fiducial approaches using their coverage probabilities and average lengths via Monte Carlo simulation. The simulation results show that the Bayesian confidence interval performed better than the others in terms of coverage probability and average length. The proposed approach and the existing approaches are illustrated using examples from data set PM10 level and PM2.5 level in the northern Thailand. DE-206 Namensnennung 4.0 International CC BY 4.0 cc https://creativecommons.org/licenses/by/4.0/ Bayesian approach (dpeaa)DE-206 coefficient of variation (dpeaa)DE-206 confidence interval (dpeaa)DE-206 log-normal distribution (dpeaa)DE-206 ratio (dpeaa)DE-206 Suparat Niwitpong verfasserin (DE-588)1154407071 (DE-627)1015696724 (DE-576)500922136 aut Enthalten in Statistics in transition Warszawa : GUS, 2000 21(2020), 5 vom: Dez., Seite 41-60 Online-Ressource (DE-627)512298068 (DE-600)2235641-1 (DE-576)281309450 2450-0291 nnns volume:21 year:2020 number:5 month:12 pages:41-60 https://www.exeley.com/statistics_in_transition/pdf/10.21307/stattrans-2020-054 Verlag kostenfrei https://doi.org/10.21307/stattrans-2020-054 Resolving-System kostenfrei http://hdl.handle.net/10419/236804 Resolving-System kostenfrei GBV_USEFLAG_U GBV_ILN_26 ISIL_DE-206 SYSFLAG_1 GBV_KXP GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_138 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_374 GBV_ILN_602 GBV_ILN_647 GBV_ILN_702 GBV_ILN_2014 GBV_ILN_2863 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 GBV_ILN_2403 GBV_ILN_2403 ISIL_DE-LFER AR 21 2020 5 12 41-60 26 01 0206 3999125756 x1z 04-11-21 2403 01 DE-LFER 400155495X 00 --%%-- --%%-- n --%%-- l01 11-11-21 2403 01 DE-LFER https://doi.org/10.21307/stattrans-2020-054 2403 01 DE-LFER https://www.exeley.com/statistics_in_transition/pdf/10.21307/stattrans-2020-054
language	English
source	Enthalten in Statistics in transition 21(2020), 5 vom: Dez., Seite 41-60 volume:21 year:2020 number:5 month:12 pages:41-60
sourceStr	Enthalten in Statistics in transition 21(2020), 5 vom: Dez., Seite 41-60 volume:21 year:2020 number:5 month:12 pages:41-60
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topic_facet	Bayesian approach coefficient of variation confidence interval log-normal distribution ratio
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container_title	Statistics in transition
authorswithroles_txt_mv	Warisa Thangjai @@aut@@ Suparat Niwitpong @@aut@@
publishDateDaySort_date	2020-12-01T00:00:00Z
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topic_title	Comparing particulate matter dispersion in Thailand using the Bayesian Confidence Intervals for ratio of coefficients of variation Warisa Thangjai, Suparat Niwitpong Bayesian approach (dpeaa)DE-206 coefficient of variation (dpeaa)DE-206 confidence interval (dpeaa)DE-206 log-normal distribution (dpeaa)DE-206 ratio (dpeaa)DE-206
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title	Comparing particulate matter dispersion in Thailand using the Bayesian Confidence Intervals for ratio of coefficients of variation
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title_full	Comparing particulate matter dispersion in Thailand using the Bayesian Confidence Intervals for ratio of coefficients of variation Warisa Thangjai, Suparat Niwitpong
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title_sort	comparing particulate matter dispersion in thailand using the bayesian confidence intervals for ratio of coefficients of variation
title_auth	Comparing particulate matter dispersion in Thailand using the Bayesian Confidence Intervals for ratio of coefficients of variation
abstract	Recently, harmful levels of air pollution have been detected in many provinces of Thailand. Particulate matter (PM) contains microscopic solids or liquid droplets that are so small that they can be inhaled and cause serious health problems. A high dispersion of PM is measured by a coefficient of variation of log-normal distribution. Since the log-normal distribution is often used to analyse environmental data such as hazardous dust particle levels and daily rainfall data. These data focus the statistical inference on the coefficient of variation. In this paper, we develop confidence interval estimation for the ratio of coefficients of variation of two log-normal distributions constructed using the Bayesian approach. These confidence intervals were then compared with the existing approaches: method of variance estimates recovery (MOVER), modified MOVER, and approximate fiducial approaches using their coverage probabilities and average lengths via Monte Carlo simulation. The simulation results show that the Bayesian confidence interval performed better than the others in terms of coverage probability and average length. The proposed approach and the existing approaches are illustrated using examples from data set PM10 level and PM2.5 level in the northern Thailand.
abstractGer	Recently, harmful levels of air pollution have been detected in many provinces of Thailand. Particulate matter (PM) contains microscopic solids or liquid droplets that are so small that they can be inhaled and cause serious health problems. A high dispersion of PM is measured by a coefficient of variation of log-normal distribution. Since the log-normal distribution is often used to analyse environmental data such as hazardous dust particle levels and daily rainfall data. These data focus the statistical inference on the coefficient of variation. In this paper, we develop confidence interval estimation for the ratio of coefficients of variation of two log-normal distributions constructed using the Bayesian approach. These confidence intervals were then compared with the existing approaches: method of variance estimates recovery (MOVER), modified MOVER, and approximate fiducial approaches using their coverage probabilities and average lengths via Monte Carlo simulation. The simulation results show that the Bayesian confidence interval performed better than the others in terms of coverage probability and average length. The proposed approach and the existing approaches are illustrated using examples from data set PM10 level and PM2.5 level in the northern Thailand.
abstract_unstemmed	Recently, harmful levels of air pollution have been detected in many provinces of Thailand. Particulate matter (PM) contains microscopic solids or liquid droplets that are so small that they can be inhaled and cause serious health problems. A high dispersion of PM is measured by a coefficient of variation of log-normal distribution. Since the log-normal distribution is often used to analyse environmental data such as hazardous dust particle levels and daily rainfall data. These data focus the statistical inference on the coefficient of variation. In this paper, we develop confidence interval estimation for the ratio of coefficients of variation of two log-normal distributions constructed using the Bayesian approach. These confidence intervals were then compared with the existing approaches: method of variance estimates recovery (MOVER), modified MOVER, and approximate fiducial approaches using their coverage probabilities and average lengths via Monte Carlo simulation. The simulation results show that the Bayesian confidence interval performed better than the others in terms of coverage probability and average length. The proposed approach and the existing approaches are illustrated using examples from data set PM10 level and PM2.5 level in the northern Thailand.
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title_short	Comparing particulate matter dispersion in Thailand using the Bayesian Confidence Intervals for ratio of coefficients of variation
url	https://www.exeley.com/statistics_in_transition/pdf/10.21307/stattrans-2020-054 https://doi.org/10.21307/stattrans-2020-054 http://hdl.handle.net/10419/236804
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Comparing particulate matter dispersion in Thailand using the Bayesian Confidence Intervals for ratio of coefficients of variation

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