Local Polynomial Regression Estimator of the Finite Population Total Under Stratified Random Sampling: A Model-Based Approach

Abstract

In this thesis, auxiliary information is used to determine an estimator of nite population total using nonparametric regression under strati ed random sampling. To achieve this, a model-based approach is adopted by making use of the local polynomial regression estimation to predict the nonsampled values of the survey variable. The performance of the proposed estimator is investigated against some design-based and model-based regression estimators. From the simulation experiments, the resulting estimator records better results in the estimation of the nite population total. Generally, use of the proposed estimator leads to relatively smaller values of relative e ciency compared to other estimators.