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).
Abstract. Copula is a probability distribution that allows a joint distribution function build from different univariate marginal distribution function. The climate in Malaysia is very humid, which cause the rainfall data is usually skewed. Gumbel, Clayton and skew t copula are distributions that good in analyze data that is extreme. Five rain gauge stations in Johor will be used in this study. The most suitable copula function that best suit the bivariate relation among the five stations will be studied. The Akaike information criterion and Bayesian information criterion will be the used as the moderators to decide the best suit copula function. Gumbel copula is the best suit copula function among the five rain gauge stations.
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