Volume 5, Issue 5, September 2016, Page: 317-325
A Multiplicative Bias Corrected Nonparametric Estimator for a Finite Population Mean
Bonface Miya Malenje, Department of Statistics and Actuarial Sciences, Jomo Kenyatta University of Agriculture and Technology, Nairobi, Kenya
Winnie Onsongo Mokeira, Department of Statistics and Actuarial Sciences, Jomo Kenyatta University of Agriculture and Technology, Nairobi, Kenya
Romanus Odhiambo, Department of Statistics and Actuarial Sciences, Jomo Kenyatta University of Agriculture and Technology, Nairobi, Kenya
George Otieno Orwa, Department of Statistics and Actuarial Sciences, Jomo Kenyatta University of Agriculture and Technology, Nairobi, Kenya
Received: Sep. 28, 2015;       Accepted: Oct. 20, 2015;       Published: Sep. 28, 2016
DOI: 10.11648/j.ajtas.20160505.21      View  3083      Downloads  97
Abstract
Nonparametric regression has been widely exploited in survey sampling to construct estimators for the finite population mean and total. It offers greater flexibility with regard to model specification and is therefore applicable to a wide range of problems. A major drawback of estimators constructed under this framework is that they are generally biased due to the boundary problem and therefore require modification at the boundary points. In this study, a bias robust estimator for the finite population mean based on the multiplicative bias reduction technique is proposed. A simulation study is performed to develop the properties of this estimator as well as assess its performance relative to other existing estimators. The asymptotic properties and coverage rates of our proposed estimator are better than those exhibited by the Nadaraya Watson estimator and the ratio estimator.
Keywords
Multiplicative Bias, Nonparametric Model, Finite Population Mean, Conditional Bias
To cite this article
Bonface Miya Malenje, Winnie Onsongo Mokeira, Romanus Odhiambo, George Otieno Orwa, A Multiplicative Bias Corrected Nonparametric Estimator for a Finite Population Mean, American Journal of Theoretical and Applied Statistics. Vol. 5, No. 5, 2016, pp. 317-325. doi: 10.11648/j.ajtas.20160505.21
Copyright
Copyright © 2016 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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