Loading...
Please wait, while we are loading the content...
Similar Documents
Gaussian Copula Marginal Regression for Modeling Extreme Data with Application
| Content Provider | Semantic Scholar |
|---|---|
| Author | Kuswanto, Heri Ratih, Dewi Kumala |
| Copyright Year | 2014 |
| Abstract | Regression is commonly used to determine the relati onship between the response variable and the predic tor variable, where the parameters are estimated by Ord inary Least Square (OLS). This method can be used w ith an assumption that residuals are normally distribut ed (0, σ). However, the assumption of normality of the data is often violated due to extreme observations, whic h are often found in the climate data. Modeling of rice harvested area with rainfall predictor variables al lows extreme observations. Therefore, another appro ximation is necessary to be applied in order to overcome the presence of extreme observations. The method used to solve this problem is a Gaussian Copula Marginal Re gression (GCMR), the regression-based Copula. As a case study, the method is applied to model rice har vested area of rice production centers in East Java , Indonesia, covering District: Banyuwangi, Lamongan, Bojonegoro, Ngawi and Jember. Copula is chosen because this method is not strict against the assum ption distribution, especially the normal distribut ion. Moreover, this method can describe dependency on ex treme point clearly. The GCMR performance will be compared with OLS and Generalized Linear Models (GL M). The identification result of the dependencies structure between the Rice Harvest per period (RH) and monthly rainfall showed a dependency in all are as of research. It is shown that the real test copula typ e mostly follows the Gumbel distribution. While the comparison of the model goodness for rice harvested ar a in the modeling showed that the method used t o model the exact GCMR in five districts RH1 and RH2 in Jember district since its lowest AICc. Looking a t the data distribution pattern of response variables, it can be concluded that the GCMR good for modeling t he response variable that is not normally distributed an tend to have a large skew. |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | http://thescipub.com/PDF/jmssp.2014.192.200.pdf |
| Language | English |
| Access Restriction | Open |
| Subject Keyword | Akaike information criterion Generalized linear model Iontophoresis Java Kerrison Predictor Least-Squares Analysis Marginal model Nevus sebaceous Normal Statistical Distribution Normality Unit Object-relational database Ordinary least squares Rice's theorem Tor Messenger adenotonsillectomy emotional dependency |
| Content Type | Text |
| Resource Type | Article |
