PENENTUAN MODEL KEMISKINAN DI JAWA TENGAH DENGAN MULTIVARIATE GEOGRAPHICALLY WEIGHTED REGRESSION (MGWR) - Sindy Saputri; Dwi Ispriyanti; Triastuti Wuryandari (Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro, 2015)
The problem of poverty is a fundamental problem faced in a number of regions in Indonesia, to determine significant indicators on poverty by taking into account the spatial variation in the province of Central Java can use multivariate models Geographically Weighted Regression (MGWR). In the model MGWR model parameter estimation is obtained by using Weighted Least Square (WLS). Selection of the optimum bandwidth using Cross Validation (CV). The study looked for the best model among MGWR with multivariate regression and create distribution maps counties and cities in the province of Central Java based variables significantly to poverty. The results of testing the suitability of the model shows that there is no influence of spatial factors on the percentage of poor and non-poor in the province of Central Java. Variables expected to affect the percentage of poor people is a variable percentage of expenditures for food, while the percentage of the non-poor is a variable percentage of expenditure on food and the percentage of heads of household education level less than SD. Based on the AIC and the MSE obtained the best model is the model MGWR with AIC value of 44.4603 and MSE 0.454.Keywords: Cross Validation, MGWR, Poverty, Weighted Least Square