Machine learning based variance estimation under two phase sampling using health and education sector data.

ABSTRACT: This study proposes a novel variance estimator [Formula: see text]under two-phase sampling, utilizing one auxiliary variable and one binary attribute to enhance estimation efficiency. Theoretical properties of the estimator were obtained, such as the formula of bias and Mean Squared Error (MSE), which proves the analytical superiority of the estimator. The empirical efficiency of the simulation was demonstrated by the simulation performance in datasets of the health and education sectors, and the MSE values are consistently lower than those of the classical and competitive estimators. In further supporting its predictive power, machine learning classifiers (Regression Tree, Random Forest, and Support Vector Regression) were also trained on the same auxiliary inputs and evaluated benchmarke

SUBMITTER: Al-Marzouki S 

PROVIDER: S-EPMC12948996 | biostudies-literature | 2026 Feb

REPOSITORIES: biostudies-literature