The usage of copula to determine the joint distribution between two variables is widely used in various areas. The joint distribution of rainfall characteristic obtained using the copula model is more ideal than the standard bivariate modelling where copula is belief to have overcome some limitation. Six copula models will be applied to obtain the most suitable bivariate distribution between two rain gauge stations. The copula models are Ali-Mikhail-Haq (AMH), Clayton, Frank, Galambos, Gumbel-Hoogaurd (GH) and Plackett. The rainfall data used in the study is selected from rain gauge stations which are located in the southern part of Peninsular Malaysia, during the period from 1980 to 2011. The goodness-of-fit test in this study is based on the Akaike information criterion (AIC).
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10 July 2014
PROCEEDINGS OF THE 21ST NATIONAL SYMPOSIUM ON MATHEMATICAL SCIENCES (SKSM21): Germination of Mathematical Sciences Education and Research towards Global Sustainability
6–8 November 2013
Penang, Malaysia
Research Article|
July 10 2014
Bivariate copula in fitting rainfall data
Kong Ching Yee;
Kong Ching Yee
Department of Mathematical Sciences, Faculty of Science, Universiti Teknologi Malaysia, 81310 UTM Johor Bahru, Johor,
Malaysia
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Jamaludin Suhaila;
Jamaludin Suhaila
Department of Mathematical Sciences, Faculty of Science, Universiti Teknologi Malaysia, 81310 UTM Johor Bahru, Johor,
Malaysia
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Fadhilah Yusof;
Fadhilah Yusof
Department of Mathematical Sciences, Faculty of Science, Universiti Teknologi Malaysia, 81310 UTM Johor Bahru, Johor,
Malaysia
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Foo Hui Mean
Foo Hui Mean
Department of Mathematical Sciences, Faculty of Science, Universiti Teknologi Malaysia, 81310 UTM Johor Bahru, Johor,
Malaysia
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Kong Ching Yee
Jamaludin Suhaila
Fadhilah Yusof
Foo Hui Mean
Department of Mathematical Sciences, Faculty of Science, Universiti Teknologi Malaysia, 81310 UTM Johor Bahru, Johor,
Malaysia
AIP Conf. Proc. 1605, 986–990 (2014)
Citation
Kong Ching Yee, Jamaludin Suhaila, Fadhilah Yusof, Foo Hui Mean; Bivariate copula in fitting rainfall data. AIP Conf. Proc. 10 July 2014; 1605 (1): 986–990. https://doi.org/10.1063/1.4887724
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