Optimal Allocation in Domains Mean Estimation Using Double Sampling with Non-Linear Cost Function in the Presence of Non-Response
American Journal of Theoretical and Applied Statistics
Volume 7, Issue 2, March 2018, Pages: 45-57
Received: Dec. 13, 2017; Accepted: Jan. 5, 2018; Published: Feb. 12, 2018
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Authors
Alilah David Anekeya, Department of Mathematics, Masinde Muliro University of Science and Technology, Kakamega, Kenya
Ouma Christopher Onyango, Departments of Statistics and Actuarial Science, Kenyatta University, Nairobi, Kenya
Nyongesa Kennedy, Department of Mathematics, Masinde Muliro University of Science and Technology, Kakamega, Kenya
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Abstract
Studies have been carried out on domain mean estimation using non-linear cost function. However little has been done on domain stratum estimation using non-linear cost function using ratio estimation in the presence of non-response. This study develops a method of optimal stratum sample size allocation in domain mean estimation using double sampling with non-linear cost function in the presence of non- response. To obtain an optimum sample size, Lagrangian multiplier technique is employed by minimizing precision at a specified cost. In the estimation of the domain mean, auxiliary variable information in which the study and auxiliary variables both suffers from non-response in the second phase sampling is used. The expressions of the biases and mean square errors of proposed estimator has also been obtained.
Keywords
Optimal Allocation, Double Sampling, Non-Linear Cost Function, Non-Response
To cite this article
Alilah David Anekeya, Ouma Christopher Onyango, Nyongesa Kennedy, Optimal Allocation in Domains Mean Estimation Using Double Sampling with Non-Linear Cost Function in the Presence of Non-Response, American Journal of Theoretical and Applied Statistics. Vol. 7, No. 2, 2018, pp. 45-57. doi: 10.11648/j.ajtas.20180702.11
Copyright
Copyright © 2018 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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