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Bayesian estimation for the mean of delta-gamma distributions with application to rainfall data in Thailand

Precipitation and flood forecasting are difficult due to rainfall variability. The mean of a delta-gamma distribution can be used to analyze rainfall data for predicting future rainfall, thereby reducing the risks of future disasters due to excessive or too little rainfall. In this study, we constru...

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Autores principales: Kaewprasert, Theerapong, Niwitpong, Sa-Aat, Niwitpong, Suparat
Formato: Online Artículo Texto
Lenguaje:English
Publicado: PeerJ Inc. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9123891/
https://www.ncbi.nlm.nih.gov/pubmed/35607452
http://dx.doi.org/10.7717/peerj.13465
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author Kaewprasert, Theerapong
Niwitpong, Sa-Aat
Niwitpong, Suparat
author_facet Kaewprasert, Theerapong
Niwitpong, Sa-Aat
Niwitpong, Suparat
author_sort Kaewprasert, Theerapong
collection PubMed
description Precipitation and flood forecasting are difficult due to rainfall variability. The mean of a delta-gamma distribution can be used to analyze rainfall data for predicting future rainfall, thereby reducing the risks of future disasters due to excessive or too little rainfall. In this study, we construct credible and highest posterior density (HPD) intervals for the mean and the difference between the means of delta-gamma distributions by using Bayesian methods based on Jeffrey’s rule and uniform priors along with a confidence interval based on fiducial quantities. The results of a simulation study indicate that the Bayesian HPD interval based on Jeffrey’s rule prior performed well in terms of coverage probability and provided the shortest expected length. Rainfall data from Chiang Mai province, Thailand, are also used to illustrate the efficacies of the proposed methods.
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spelling pubmed-91238912022-05-22 Bayesian estimation for the mean of delta-gamma distributions with application to rainfall data in Thailand Kaewprasert, Theerapong Niwitpong, Sa-Aat Niwitpong, Suparat PeerJ Statistics Precipitation and flood forecasting are difficult due to rainfall variability. The mean of a delta-gamma distribution can be used to analyze rainfall data for predicting future rainfall, thereby reducing the risks of future disasters due to excessive or too little rainfall. In this study, we construct credible and highest posterior density (HPD) intervals for the mean and the difference between the means of delta-gamma distributions by using Bayesian methods based on Jeffrey’s rule and uniform priors along with a confidence interval based on fiducial quantities. The results of a simulation study indicate that the Bayesian HPD interval based on Jeffrey’s rule prior performed well in terms of coverage probability and provided the shortest expected length. Rainfall data from Chiang Mai province, Thailand, are also used to illustrate the efficacies of the proposed methods. PeerJ Inc. 2022-05-18 /pmc/articles/PMC9123891/ /pubmed/35607452 http://dx.doi.org/10.7717/peerj.13465 Text en © 2022 Kaewprasert et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ) and either DOI or URL of the article must be cited.
spellingShingle Statistics
Kaewprasert, Theerapong
Niwitpong, Sa-Aat
Niwitpong, Suparat
Bayesian estimation for the mean of delta-gamma distributions with application to rainfall data in Thailand
title Bayesian estimation for the mean of delta-gamma distributions with application to rainfall data in Thailand
title_full Bayesian estimation for the mean of delta-gamma distributions with application to rainfall data in Thailand
title_fullStr Bayesian estimation for the mean of delta-gamma distributions with application to rainfall data in Thailand
title_full_unstemmed Bayesian estimation for the mean of delta-gamma distributions with application to rainfall data in Thailand
title_short Bayesian estimation for the mean of delta-gamma distributions with application to rainfall data in Thailand
title_sort bayesian estimation for the mean of delta-gamma distributions with application to rainfall data in thailand
topic Statistics
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9123891/
https://www.ncbi.nlm.nih.gov/pubmed/35607452
http://dx.doi.org/10.7717/peerj.13465
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