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Non-parametric Variance Estimation Using Donor Imputation Method
American Journal of Theoretical and Applied Statistics
Volume 5, Issue 5, September 2016, Pages: 252-259
Received: Jul. 1, 2016; Accepted: Jul. 16, 2016; Published: Aug. 3, 2016
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Hellen W. Waititu, Department of Statistics and Computer Sciences, Moi University, Nairobi, Kenya
Edward Njenga, Department of Mathematics, Kenyatta University, Nairobi, Kenya
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The main objective of this study is to investigate the relative performance of donor imputation method in situations that are likely to occur in practice and to carry out numerical comparative study of estimators of variance using Nadaraya-Watson kernel estimators and other estimators. Nadaraya-Watson kernel estimator can be viewed as a non-parametric imputation method as it leads to an imputed estimator with negligible bias without requiring the specification of a parametric imputation model. Simulation studies were carried out to investigate the performance of Nadaraya-Watson kernel estimators in terms of variance. From the results, it was found out that Nadaraya-Watson kernel estimator has negligible bias and its variance is small. When compared with Naïve, Jackknife and Bootstrap estimators, Nadaraya-Watson kernel estimator was found to perform better than bootstrap estimator in linear and non-linear populations.
Hot Deck Imputation, Non-parametric, Unbiased Estimator, Donor, Recipient, Donor Imputation
To cite this article
Hellen W. Waititu, Edward Njenga, Non-parametric Variance Estimation Using Donor Imputation Method, American Journal of Theoretical and Applied Statistics. Vol. 5, No. 5, 2016, pp. 252-259. doi: 10.11648/j.ajtas.20160505.11
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This article is an open access article distributed under the Creative Commons Attribution License ( which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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