Statisticians use survey sampling methods in the estimation of population parameters of interest.
This field has received increased demand due to the reliable statistic they produce. Information
is extracted from the samples and used to make inferences about the population and used for planning purposes. This information is collected either by survey sampling or census. However,
census is an expensive and tedious method to carry out in the estimation process thus preferring
survey sampling in estimation. In survey sampling, estimation can be either parametric or nonparametric. In nonparametric, estimation of finite population total divides into the sampled
and non-sampled parts. Estimation of the sampled part is quite easy thus the problem reduces to
the estimation of non-sampled part. Different approaches have been used by statisticians in the estimation of the non-sampled part. These approaches have however relied on the use kernel
smoothers and has been known to suffer the problem of boundary bias. In this study, a
nonparametric estimator for a finite population total that addresses this drawback of kernel smoothers is proposed. The properties of this estimator were studied empirically in order to
determine its efficiency. The estimator was applied to a simulated data and comparative analysis
was done using R statistical software version i386 4.0.3 and the results of the bias were confirmed. The performance of the proposed estimator was tested and compared against the
design-based Horvitz-Thompson estimator, the model-based approach proposed by Dorfman

and the ratio estimator. The proposed estimator was developed by modifying the Nadaraya- Watson kernel estimator using two boundary bias reducing techniques. The bias, variance and

Mean Squared Error of the estimator were studied theoretically and applied to an empirical study out using simulated data from linear, quadratic and exponential mean functions. Both the
unconditional and conditional properties of the estimators under the three mean functions were
investigated. The proposed estimator outperformed the ratio estimator, Horvitz-Thompson estimator and the estimator due to Dorfman in quadratic and exponential mean functions. This
is evident from the small biases and mean squared error values obtained. For the linear mean
function, the ratio estimator gave the best estimates because it is (BLUE). Therefore, the proposed nonparametric estimator for a finite population total was developed, the asymptotic
properties were studied and comparative analysis done using simulated data. From the results obtained, the proposed estimator was found to give smaller biases and therefore can be
recommended for bias correction at the boundary. The proposed estimator in this study is based
on stratified sampling, thus a study using cluster sampling is recommended to compare the performance of the estimator and further research to improve the estimator to work for all
theoretical data variables.