Comparison of Robust Regression Methods: Least Trimmed Squares and Maximum Likelihood for Handling Outliers - Kurniawan, Andro; Oktarina, Cinta Rizki; Sabarinsyah, Sabarinsyah (UNIB Press, 2025)
This study investigates the determinants of per capita expenditure in 154 regencies and cities across Sumatra Island. The use of the Ordinary Least Squares method is deemed inappropriate due to violations of classical assumptions and the presence of outliers within the dataset. To address these issues, robust regression approaches are applied, specifically M-estimation and Least Trimmed Squares (LTS). The dependent variable in the analysis is per capita expenditure, while the explanatory variables include poverty line, human development index, average years of schooling, and expected years of schooling. The estimation procedures are performed using both raw and standardized data. The empirical results demonstrate that each independent variable significantly influences per capita expenditure under both robust estimation techniques. To determine the most reliable method, the residual standard error is used as the evaluation criterion. The outcomes indicate that the LTS estimator applied to standardized data provides the lowest error value, suggesting that it is the most suitable approach for estimating the regression parameters associated with per capita expenditure in Sumatra.