VARIABLE SELECTION IN ANALYZING LIFE INFANT BIRTH IN INDONESIA USING GROUP LASSO AND GROUP SCAD

Regression analysis often requires a selection of explanatory variables X1, X2, ... Xp so shrinkage coefficients can occur that can facilitate the interpretation of the regression equation obtained. In this context, the explanatory variable often has a grouping structure so that a more relevant problem is how to choose groups rather than individuals. Group LASSO and group SCAD are techniques for selecting groups of variables which in many works of literature appear to have advantages over LASSO. In this study, the percentage of live born children in the province of Bali, East Nusa Tenggara and other Indonesia provinces were analyzed and linked to the explanatory variables using the group LASSO and group SCAD methods. The classification of available explanatory variables is grouped based on the theory and results of previous studies. The results show that the best model is the group SCAD method with the smallest AIC, BIC and GCV values. Factors included in the model for Bali province are demographic factors, women's status, and autonomy and the economy. For East Nusa Tenggara province the factors that enter the model are demographics and economics, while generally for Indonesia the factors that are included in the model are demography, women's status, and autonomy and family planning.