TITLE:
Local Polynomial Regression Estimator of the Finite Population Total under Stratified Random Sampling: A Model-Based Approach
AUTHORS:
Charles K. Syengo, Sarah Pyeye, George O. Orwa, Romanus O. Odhiambo
KEYWORDS:
Sample Surveys, Stratified Random Sampling, Auxiliary Information, Local Polynomial Regression, Model-Based Approach, Nonparametric Regression
JOURNAL NAME:
Open Journal of Statistics,
Vol.6 No.6,
December
8,
2016
ABSTRACT: In this paper, auxiliary information is
used to determine an estimator of finite population total using nonparametric
regression under stratified 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 y. The performance of
the proposed estimator is investigated against some design-based and
model-based regression estimators. The simulation experiments show that the
resulting estimator exhibits good properties. Generally, good confidence
intervals are seen for the nonparametric regression estimators, and use of the
proposed estimator leads to relatively smaller values of RE compared to other
estimators.