PEMODELAN SEMIPARAMETRIK STATISTICAL DOWNSCALING UNTUK MENDUGA CURAH HUJAN BULANAN DI INDRAMAYU - Akbar Rizki; Abdul Aziz Nurussadad (Xplore: Journal of Statistics, 2018)
Semiparametric statistical downscaling (SD) model is a statistical model which consists of parametric and non-parametric functional relationship between local scale and global scale variable. This study used rainfall intensity in Indramayu as local scale variable and Global Precipitation Climatology Project (GPCP) precipitation as global scale variable. GPCP precipitation data have multicollinearity, therefore they were reduced by principal component analysis. Eight principal components which have been selected then used as the prediktors and rainfall intensity in Indramayu as the response. Semiparametric SD model was used to predict the rainfall intensity in the district of Indramayu. The semiparametric model developed by mixed model approach where the nonparametric relationship is represented using spline with truncated power basis. Linier semiparametric model is the best model to estimate monthly rainfall in indramayu district. The model performance evaluated by RMSEP (root mean square error prediction) and (coefficient of determination). The result shows that the best model have values of RMSEP and are 61.64 and 71%.