Parametric bootstrap confidence interval estimation for the percentile and difference between the percentiles of delta-lognormal distributions with application to rainfall data in Thailand

In Thailand, flooding often occurs during the summer monsoon when many tropical storms affect the country. The motivation of this study was to plan for and mitigate the damage caused by flooding in the future. The confidence interval (CI) for the percentile of a precipitation dataset can be used to...

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主要な著者: Warisa Thangjai,, Sa-Aat Niwitpong,, Suparat Niwitpong,
フォーマット: 論文
言語:English
出版事項: Penerbit Universiti Kebangsaan Malaysia 2023
オンライン・アクセス:http://journalarticle.ukm.my/23255/1/SB%2019.pdf
http://journalarticle.ukm.my/23255/
https://www.ukm.my/jsm/english_journals/vol52num11_2023/contentsVol52num11_2023.html
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spelling my-ukm.journal.232552024-03-22T01:05:26Z http://journalarticle.ukm.my/23255/ Parametric bootstrap confidence interval estimation for the percentile and difference between the percentiles of delta-lognormal distributions with application to rainfall data in Thailand Warisa Thangjai, Sa-Aat Niwitpong, Suparat Niwitpong, In Thailand, flooding often occurs during the summer monsoon when many tropical storms affect the country. The motivation of this study was to plan for and mitigate the damage caused by flooding in the future. The confidence interval (CI) for the percentile of a precipitation dataset can be used to estimate the intensity of rainfall in a particular area whereas the CI for the difference between the percentiles of two datasets can be used to compare the rainfall intensities in two areas. To this end, the performances of several approaches to estimate the CI for the percentile and difference between the percentiles of delta-lognormal distributions were constructed and compared. These estimates were constructed based on the Bayesian (BS) and parametric bootstrap (PB) approaches, as well as two fiducial generalized confidence interval (FGCI) approaches. The performances of the methods were evaluated using Monte Carlo simulation, the results of which indicate that the PB approach for both CIs performed the best in all scenarios tested. Its suitability was confirmed via two illustrative examples using daily rainfall datasets for Chiang Mai and Lampang provinces in Thailand. Penerbit Universiti Kebangsaan Malaysia 2023 Article PeerReviewed application/pdf en http://journalarticle.ukm.my/23255/1/SB%2019.pdf Warisa Thangjai, and Sa-Aat Niwitpong, and Suparat Niwitpong, (2023) Parametric bootstrap confidence interval estimation for the percentile and difference between the percentiles of delta-lognormal distributions with application to rainfall data in Thailand. Sains Malaysiana, 52 (11). pp. 3273-3292. ISSN 0126-6039 https://www.ukm.my/jsm/english_journals/vol52num11_2023/contentsVol52num11_2023.html
institution Universiti Kebangsaan Malaysia
building Tun Sri Lanang Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Kebangsaan Malaysia
content_source UKM Journal Article Repository
url_provider http://journalarticle.ukm.my/
language English
description In Thailand, flooding often occurs during the summer monsoon when many tropical storms affect the country. The motivation of this study was to plan for and mitigate the damage caused by flooding in the future. The confidence interval (CI) for the percentile of a precipitation dataset can be used to estimate the intensity of rainfall in a particular area whereas the CI for the difference between the percentiles of two datasets can be used to compare the rainfall intensities in two areas. To this end, the performances of several approaches to estimate the CI for the percentile and difference between the percentiles of delta-lognormal distributions were constructed and compared. These estimates were constructed based on the Bayesian (BS) and parametric bootstrap (PB) approaches, as well as two fiducial generalized confidence interval (FGCI) approaches. The performances of the methods were evaluated using Monte Carlo simulation, the results of which indicate that the PB approach for both CIs performed the best in all scenarios tested. Its suitability was confirmed via two illustrative examples using daily rainfall datasets for Chiang Mai and Lampang provinces in Thailand.
format Article
author Warisa Thangjai,
Sa-Aat Niwitpong,
Suparat Niwitpong,
spellingShingle Warisa Thangjai,
Sa-Aat Niwitpong,
Suparat Niwitpong,
Parametric bootstrap confidence interval estimation for the percentile and difference between the percentiles of delta-lognormal distributions with application to rainfall data in Thailand
author_facet Warisa Thangjai,
Sa-Aat Niwitpong,
Suparat Niwitpong,
author_sort Warisa Thangjai,
title Parametric bootstrap confidence interval estimation for the percentile and difference between the percentiles of delta-lognormal distributions with application to rainfall data in Thailand
title_short Parametric bootstrap confidence interval estimation for the percentile and difference between the percentiles of delta-lognormal distributions with application to rainfall data in Thailand
title_full Parametric bootstrap confidence interval estimation for the percentile and difference between the percentiles of delta-lognormal distributions with application to rainfall data in Thailand
title_fullStr Parametric bootstrap confidence interval estimation for the percentile and difference between the percentiles of delta-lognormal distributions with application to rainfall data in Thailand
title_full_unstemmed Parametric bootstrap confidence interval estimation for the percentile and difference between the percentiles of delta-lognormal distributions with application to rainfall data in Thailand
title_sort parametric bootstrap confidence interval estimation for the percentile and difference between the percentiles of delta-lognormal distributions with application to rainfall data in thailand
publisher Penerbit Universiti Kebangsaan Malaysia
publishDate 2023
url http://journalarticle.ukm.my/23255/1/SB%2019.pdf
http://journalarticle.ukm.my/23255/
https://www.ukm.my/jsm/english_journals/vol52num11_2023/contentsVol52num11_2023.html
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