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parametric estimation methods for bivariate copula in rainfall application

This study focuses on the parametric methods: maximum likelihood (ML), inference function of margins (IFM), and adaptive maximization by parts (AMBP) in estimating copula dependence parameter. Their performance is compared through simulation and empirical studies. For the empirical study, 44 years o...

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Main Authors: Mohd. Lokoman, Rahmah, Yusof, Fadhilah
Format: Article
Language:English
Published: Penerbit UTM Press 2019
Subjects:
QA Mathematics
Online Access:http://eprints.utm.my/id/eprint/84716/1/FadhilahYusof2019_ParametricEstimationMethodsforBivariateCopula.pdf
http://eprints.utm.my/id/eprint/84716/
https://dx.doi.org/10.11113/jt.v81.12059
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spelling my.utm.847162020-02-27T04:53:42Z http://eprints.utm.my/id/eprint/84716/ parametric estimation methods for bivariate copula in rainfall application Mohd. Lokoman, Rahmah Yusof, Fadhilah QA Mathematics This study focuses on the parametric methods: maximum likelihood (ML), inference function of margins (IFM), and adaptive maximization by parts (AMBP) in estimating copula dependence parameter. Their performance is compared through simulation and empirical studies. For the empirical study, 44 years of daily rainfall data of Station Kuala Krai and Station Ulu Sekor were used. The correlation of the two stations is statistically significant at 0.4137. The results from the simulation study show that when the sample size is small (n <1000) for correlation level less than 0.80, IFM has the best performance. While, when the sample size is large (n ≥ 1000) for any correlation level, AMBP has the best performance. The results from the empirical study also show that AMBP has the best performance when the sample size is large. Thus, in order to estimate a precise Copula dependence parameter, it can be concluded that for parametric approaches, IFM is preferred for small sample size and has correlation level less than 0.80 and AMBP is preferred for larger sample size and for any correlation level. The results obtained in this study highlight the importance of estimating the dependence structure of the hydrological data. By using the fitted copula, the Malaysian Meteorological Department will be able to generate hydrological events for a system performance analysis such as flood and drought control system. Penerbit UTM Press 2019 Article PeerReviewed application/pdf en http://eprints.utm.my/id/eprint/84716/1/FadhilahYusof2019_ParametricEstimationMethodsforBivariateCopula.pdf Mohd. Lokoman, Rahmah and Yusof, Fadhilah (2019) parametric estimation methods for bivariate copula in rainfall application. Jurnal Teknologi, 81 (1). pp. 1-10. ISSN 2180–3722 https://dx.doi.org/10.11113/jt.v81.12059 DOI:10.11113/jt.v81.12059
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
language English
topic QA Mathematics
spellingShingle QA Mathematics
Mohd. Lokoman, Rahmah
Yusof, Fadhilah
parametric estimation methods for bivariate copula in rainfall application
description This study focuses on the parametric methods: maximum likelihood (ML), inference function of margins (IFM), and adaptive maximization by parts (AMBP) in estimating copula dependence parameter. Their performance is compared through simulation and empirical studies. For the empirical study, 44 years of daily rainfall data of Station Kuala Krai and Station Ulu Sekor were used. The correlation of the two stations is statistically significant at 0.4137. The results from the simulation study show that when the sample size is small (n <1000) for correlation level less than 0.80, IFM has the best performance. While, when the sample size is large (n ≥ 1000) for any correlation level, AMBP has the best performance. The results from the empirical study also show that AMBP has the best performance when the sample size is large. Thus, in order to estimate a precise Copula dependence parameter, it can be concluded that for parametric approaches, IFM is preferred for small sample size and has correlation level less than 0.80 and AMBP is preferred for larger sample size and for any correlation level. The results obtained in this study highlight the importance of estimating the dependence structure of the hydrological data. By using the fitted copula, the Malaysian Meteorological Department will be able to generate hydrological events for a system performance analysis such as flood and drought control system.
format Article
author Mohd. Lokoman, Rahmah
Yusof, Fadhilah
author_facet Mohd. Lokoman, Rahmah
Yusof, Fadhilah
author_sort Mohd. Lokoman, Rahmah
title parametric estimation methods for bivariate copula in rainfall application
title_short parametric estimation methods for bivariate copula in rainfall application
title_full parametric estimation methods for bivariate copula in rainfall application
title_fullStr parametric estimation methods for bivariate copula in rainfall application
title_full_unstemmed parametric estimation methods for bivariate copula in rainfall application
title_sort parametric estimation methods for bivariate copula in rainfall application
publisher Penerbit UTM Press
publishDate 2019
url http://eprints.utm.my/id/eprint/84716/1/FadhilahYusof2019_ParametricEstimationMethodsforBivariateCopula.pdf
http://eprints.utm.my/id/eprint/84716/
https://dx.doi.org/10.11113/jt.v81.12059
_version_ 1662754297278889984
score 13.211869

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