Selection of variables based on nonconcave penalized likelihood using lasso, elastic net, and SCAD method Available to Purchase

Variable selection is essential in linear regression analysis to improve predictability and select significant variables. Estimating the regression coefficient on high-dimensional data cannot be done using the least squares method, so it requires specific analytical techniques. Approaches that can take on high-dimensional data include SCAD, LASSO, and Elastic Net. This research will analyze the most crucial method between SCAD, LASSO, and Elastic Net on Low Birth Weight (LBW) data in East Nusa Tenggara (NTT). Two methods are used in this study, first, comparing the SCAD, LASSO, and Elastic Net methods using simulation data, and second, applying the logistic regression method to actual data. The data used in this study is the LBW data by fertile women in NTT from the 2017 IDHS (Indonesian Demographic and Health Survey) data. The analysis shows that the results obtained through simulation and data reveal, based on the value of the AIC model goodness test, the SCAD is better than the other methods with the smallest AIC value of 17. 58878, smaller than the AIC LASSO value of 17.90169 and Elnet of 17.88728.