The Province of East Java has its own characteristics that differentiate it from any other regions. Dissimilarities in characteristics of a region may encompass issues such as social, economic, cultural, parenting, education, and the environment, so as to cause the difference in case of severe under nutrition between one region to another. Sufferers of malnutrition in one region may be linked and influenced by the surrounding regions. Therefore, we need a statistical modeling that is able to take into account the spatial factor. Statistical methods that can be used to analyze the data and also takes into account the spatial factor are the Geographically Weighted Regression (GWR). This study is aimed to determine the case of malnutrition models in East Java Province using GWR model with kernel adaptive bi-square weighting and comparing it to the conventional linear regression model. The data used in the study are secondary data obtained from the National Socio-Economic Survey and Basic Health Research (2010) conducted in 38 districts in East Java. Estimation is done by using the Weighted Least Squares method that provides different weighting values to each region. The result showed that there are 38 models of the malnutrition case that is different for each district in East Java. The GWR model with bi-square kernel weighting function is better in modelling the case of malnutrition in East Java compared to the conventional linear regression models that are based on the criteria of goodness that is the R-square, Mean Square Error and the Akaike Information Criterion.



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