International Journal of Statistics and Applied Mathematics
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International Journal of Statistics and Applied Mathematics

2020, Vol. 5, Issue 1, Part A

On local polynomial regression estimators in finite populations


Author(s): Conlet Biketi Kikechi

Abstract: This article examines the local polynomial regression estimators for the mean regression functions m ̅_0 (x_j ) and m ̅_1 (x_j ) in finite populations under circumstances when the order of the local polynomial being fit p is 0 and 1 respectively. The study utilizes a measure of performance that centres on the efficiency of the estimators of the mean regression function in survey sampling theory. To a greater extent, the examination considers analytical comparisons of the estimators in line with the concept of asymptotic relative efficiency. Particularly, asymptotic properties of the local constant regression estimator of the mean regression function are studied in a model based framework. The results of the local constant regression estimator of the mean regression function m ̅_0 (x_j ) are compared with those of the local linear regression estimator of the mean regression function m ̅_1 (x_j ) studied by Kikechi et al. (2017). Variance comparisons are made using the local constant regression estimator m ̅_0 (x_j ) and the local linear regression estimator m ̅_1 (x_j ) which show that the estimators are asymptotically equivalently efficient.

Pages: 58-63 | Views: 176 | Downloads: 12

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How to cite this article:
Conlet Biketi Kikechi. On local polynomial regression estimators in finite populations. Int J Stat Appl Math 2020;5(1):58-63.
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