Abstract:
: In survey sampling statisticians often make estimation of population parameters. This can be done using a number
of the available approaches which include design-based, model-based, model-assisted or randomization-assisted model based
approach. In this paper regression estimation under model based approach has been studied. In regression estimation,
researchers can opt to use parametric or nonparametric estimation technique. Because of the challenges that one can encounter
as a result of model misspecification in the parametric type of regression, the nonparametric regression has become popular
especially in the recent past. This paper explores this type of regression estimation. Kernel estimation usually forms an integral
part in this type of regression. There are a number of functions available for such a use. The goal of this study is to compare the
performance of the different nonparametric regression estimators (the finite population total estimator due Dorfman (1992), the
proposed finite population total estimator that incorporates reflection technique in modifying the kernel smoother), the ratio
estimator and the design-based Horvitz-Thompson estimator. To achieve this, data was simulated using a number of commonly
used models. From this data the assessment of the estimators mentioned above has been done using the conditional biases.
Confidence intervals have also been constructed with a view to determining the better estimator of those studied. The findings
indicate that proposed estimator of finite population total that is nonparametric and uses data reflection technique is better in
the context of the analysis done.
