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Bayesian Confidence Intervals for Coefficients of Variation of PM10 Dispersion

Warisa Thangjai, Sa-Aat Niwitpong, Suparat Niwitpong

Abstract


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

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Keywords


Bayesian Approach; Coefficient of Variation; Difference; Log-normal Distribution; Monte Carlo Simulation.

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Full Text: PDF

DOI: 10.28991/esj-2021-01264

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Copyright (c) 2021 Dr. Warisa Thangjai, Professor Sa-Aat Niwitpong, Associate Professor Suparat Niwitpong