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Journal : Informatika Pertanian

Statistical Downscaling to Predict Monthly Rainfall Using Generalized Linear Model with Gamma Distribution Soleh, Agus M
Informatika Pertanian Vol 24, No 2 (2015): Desember 2015
Publisher : Sekretariat Badan Penelitian dan Pengembangan Pertanian

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (716.358 KB) | DOI: 10.21082/ip.v24n2.2015.p215-222

Abstract

Statistical Downscaling (SDS) models might involve ill-conditioned covariates (large dimension and high correlation/multicollinear). This problem could be solved by a variable selection technique using L1 regularization/LASSO or a dimension reduction approach using principal component analysis (PCA). In this paper, both methods were applied to generalized linear modeling with gamma distribution and compared to predict rainfall models at 11 rain posts in Indramayu. More over, generalized linear model with gamma distribution was used to obtain non-negative rainfall prediction and compared with principal component regression (PCR). Two types of ill-conditioned data with different characteristics (CMIP5 and GPCP version 2.2) were used as covariates in SDS modeling. The results show that three methods (PCR, Gamma-PC, and Gamma-L1) did not demonstrate significant difference in term of Root Mean Square Error (RMSE) after addition of dummy variables (month) in the models. However, a generalized linear modeling with gamma distribution could be considered as the best methods since it provided non-negative rainfall predictions.
PENGENDALIAN KOEFISIEN REGRESI LEAST ABSOLUTE DEVIATION PADA RENTANG BERMAKNA MENGGUNAKAN PROGRAM LINIER Setyono, Setyono; Soleh, Agus Mohamad; Rochman, Nur
Informatika Pertanian Vol 27, No 1 (2018): Juni 2018
Publisher : Sekretariat Badan Penelitian dan Pengembangan Pertanian

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1441.468 KB) | DOI: 10.21082/ip.v27n1.2018.p25-34

Abstract

So far, regression analysis is used to model the mean of response variable as a function of some independent variables, using the least squares (LS) method. In general, the LS method is able to describe well the measure of central tendency, however it is not robust against outliers. Therefore, in certain cases, a regression analysis that minimizes the sum of absolute residuals (least absolute deviation - LAD) is required, which is more robust against outliers. So far, the value of the regression coefficient is not modeled and only depends entirely on the data processed. In some cases, the sign and the value of regression coefficients need to be controlled, in order to be in the meaningful range. The results of this study showed that the modification of the constraints on the LAD regression able to control the regression coefficients to be in the meaningful range. The results of bootstrap showed that distribution of controlled regression coefficients were different from distribution of uncontrolled regression coefficients.