Bayesian Confidence Intervals for Coefficients of Variation of PM10 Dispersion

Thangjai, Warisa; Niwitpong, Sa-Aat; Niwitpong, Suparat

doi:10.28991/esj-2021-01264

Cited by 7 publications

(4 citation statements)

References 21 publications

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“…The FGCI and Bayesian approaches were derived using a simulation technique whereas the adjusted MOVER approach was derived using formulas. The results in this investigation are similar to those of Harvey & van der Merwe (2012) ; Rao & D’Cunha (2016) , Thangjai & Niwitpong (2020b) , Thangjai, Niwitpong & Niwitpong (2021a) and Thangjai, Niwitpong & Niwitpong (2021b) .…”

Section: Discussionsupporting

confidence: 86%

“… Thangjai, Niwitpong & Niwitpong (2020c) estimated Bayesian credible intervals for the means of normal distributions with unknown coefficients of variation. Thangjai, Niwitpong & Niwitpong (2021a) applied Bayesian credible intervals for the coefficients of variation of PM10 dispersion datasets. Thangjai, Niwitpong & Niwitpong (2021b) applied the Bayesian approach for estimating the coefficients of variation of normal distributions.…”

Section: Introductionmentioning

confidence: 99%

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Estimation of common percentile of rainfall datasets in Thailand using delta-lognormal distributions

Thangjai

Niwitpong

2022

PeerJ

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Weighted percentiles in many areas can be used to investigate the overall trend in a particular context. In this article, the confidence intervals for the common percentile are constructed to estimate rainfall in Thailand. The confidence interval for the common percentile help to indicate intensity of rainfall. Herein, four new approaches for estimating confidence intervals for the common percentile of several delta-lognormal distributions are presented: the fiducial generalized confidence interval, the adjusted method of variance estimates recovery, and two Bayesian approaches using fiducial quantity and approximate fiducial distribution. The Monte Carlo simulation was used to evaluate the coverage probabilities and average lengths via the R statistical program. The proposed confidence intervals are compared in terms of their coverage probabilities and average lengths, and the results of a comparative study based on these metrics indicate that one of the Bayesian confidence intervals is better than the others. The efficacies of the approaches are also illustrated by applying them to daily rainfall datasets from various regions in Thailand.

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Section: Discussionsupporting

confidence: 86%

Section: Introductionmentioning

confidence: 99%

Estimation of common percentile of rainfall datasets in Thailand using delta-lognormal distributions

Thangjai

Niwitpong

2022

PeerJ

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“…Changes in air pollution concentrations are affected by the combined effects of anthropogenic sources and meteorological conditions, and scholars have contributed many methods in the process of studying the characteristics of pollutants (Yin et al, 2021 ; Xue et al, 2021 ; Thangjai et al, 2021 ; Ma et al, 2019 ; Feistel & Hellmuth, 2021 ). Since the COVID-19 outbreak, domestic and foreign /scholars have performed a significant amount of research to determine changes of air pollutants during the epidemic.…”

Section: Introductionmentioning

confidence: 99%

Changes of air pollutants and simulation of a heavy pollution process during COVID-19 in Shenyang

Zhao

Wei

2022

Environ Monit Assess

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During COVID-19, Shenyang implemented strict household isolation measures, resulting in a sharp reduction in anthropogenic emission sources, providing an opportunity to explore the impact of human activities on air pollution. The period from January to April of 2020 was divided into normal period, blockade period and resumption period. Combined with meteorological and pollutant data, mathematical statistics and spatial analysis methods were used to compare with the same period of 2015–2019. The results showed that PM 2.5 , PM 10 , NO 2 and O 3 increased by 32.6%, 13.2%, 4.65% and 22.7% in the normal period, among which the western area changed significantly. During the blockade period, the concentration of pollutants decreased by 35.79%, 35.87%, 32.45% and -4.84%, of which the central area changed significantly. During the resumption period, the concentration of pollutants increased by 21.8%, 8.7%, 5.7% and -6.3%, and the area with the largest change was located in the western. During the blockade period, a heavy pollution occurred with PM 2.5 as the main pollutant. The WRF-Chem model and the HYSPLIT model were used to reproduce the pollution occurrence process. The result showed that winds circulated as zonal winds during the pollution process at high altitudes. These winds were controlled by straight westerly and weak northwesterly airflows in front of the high pressure, and the ground was located behind the warm low pressure. Weather conditions were relatively stable. Thus, high temperatures (average > 10 ℃), high humidity (40%-60%) and slow wind (2 m/s) conditions prevailed for a long time in the Shenyang area. The unfavorable meteorological conditions lead to the occurrence of pollution. The backward trajectory showed that the potential source areas were concentrated in the urban agglomeration around Shenyang, and sporadic contributions came from North Korea.

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“…In addition, Thangjai and Niwitpong [16] established confidence intervals for the signal-to-noise ratio of lognormal distributions using the GCI, large-sample, and MOVER approaches and recommended the GCI approach under all of the circumstances tested. Recently, Thangjai et al [17] suggested the Bayesian approach for confidence interval construction for both a single lognormal CV and the difference between the CVs of two lognormal distributions.…”

Section: -Introductionmentioning

confidence: 99%

Confidence Intervals for the Coefficient of Quartile Variation of a Zero-inflated Lognormal Distribution

Yosboonruang

Niwitpong

2021

Emerg Sci J

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There are many types of skewed distribution, one of which is the lognormal distribution that is positively skewed and may contain true zero values. The coefficient of quartile variation is a statistical tool used to measure the dispersion of skewed and kurtosis data. The purpose of this study is to establish confidence and credible intervals for the coefficient of quartile variation of a zero-inflated lognormal distribution. The proposed approaches are based on the concepts of the fiducial generalized confidence interval, and the Bayesian method. Coverage probabilities and expected lengths were used to evaluate the performance of the proposed approaches via Monte Carlo simulation. The results of the simulation studies show that the fiducial generalized confidence interval and the Bayesian based on uniform and normal inverse Chi-squared priors were appropriate in terms of the coverage probability and expected length, while the Bayesian approach based on Jeffreys' rule prior can be used as alternatives. In addition, real data based on the red cod density from a trawl survey in New Zealand is used to illustrate the performances of the proposed approaches. Doi: 10.28991/esj-2021-01289 Full Text: PDF

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Bayesian Confidence Intervals for Coefficients of Variation of PM10 Dispersion

Cited by 7 publications

References 21 publications

Estimation of common percentile of rainfall datasets in Thailand using delta-lognormal distributions

Estimation of common percentile of rainfall datasets in Thailand using delta-lognormal distributions

Changes of air pollutants and simulation of a heavy pollution process during COVID-19 in Shenyang

Confidence Intervals for the Coefficient of Quartile Variation of a Zero-inflated Lognormal Distribution

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