Authors : B. S. Kambo

Volume/Issue : Volume 11 - 2026, Issue 9 - September

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Google Scholar : https://tinyurl.com/32y9emu5

DOI : https://doi.org/10.38124/ijisrt/26sep760

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

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Abstract : Background: Ratio estimators in survey sampling leverage auxiliary variables correlated with the target variable to reduce sample variance. When auxiliary variables are generated from predictive machine learning models, optimizing the transformation exponent K offers substantial precision gains over classical ratio methods.  Objectives: This study formulates and empirically evaluates a Generalized Power Ratio Estimator under both Stratified Simple Random Sampling Without Replacement (SRSWOR) and Stratified Probability Proportional to Size with Replacement (PPSWR). Furthermore, we integrate Stratified Jackknife Technique to address small-sample ratio bias.

Keywords : Power Ratio Estimator, Jackknife, Calibration, Pre, Model Assisted Approach, Svr, Auxilary Variable.

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