Abstract:
This study explores the estimation of finite population total. For many years design-based approach dominated the
scene in statistical inference in sample surveys. The scenario has since changed with emergence of the other approaches
(Model-Based, Model-Assisted and the Randomization-Assisted), which have proved to rival the conventional approach.
This paper focuses on a model based approach. Within this framework a nonparametric regression estimator for finite
population total is developed. The nonparametric technique has been found from previous studies to be advantageous
than its parametric counterpart in terms of robustness and flexibility. Kernel smoother has been used in construction of
the estimator. The challenge of the boundary problem encountered with the Nadaraya-Watson estimator has been
addressed by modifying it using reflection technique. The performance of the proposed estimator has been compared to
the design-based Horvitz Thompson estimator and the model –based nonparametric regression estimator proposed by
(Dorfman, 1992) and the ratio estimator using simulated data.
