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Estimation of Finite Population Mean Using Ratio Estimator Based on Known Median of Auxiliary Variable in the Presence of Non-Response

Received: 11 March 2022     Accepted: 1 April 2022     Published: 8 April 2022
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Abstract

The issue of non-response is a common phenomenon in sample surveys. Therefore, there is a need to develop ways of dealing with the challenge whenever it occurs. The current paper first introduces the stratification of the population as a result of the non-response. A theoretical review of the basic non response in sampling is as well explained and derived. The condition that leads to the first non-response estimator as proposed by the Hansen and Hurwitz. The resampling scheme for the non-response adjustment was described. This forms the bases for the new model which proposes a modified ratio estimator of the finite population mean in the presence of non-response when the population median of the auxiliary variable is known. The properties of the proposed estimators are derived and theoretically compared with existing ones. A theoretical efficiency comparison shows that the proposed estimator performs better than the existing ones. Further, the simulated numerical comparison shows that the Bias of the proposed estimator performs better, while its Mean squared error is competitive. Towards, the conclusion of the study we recommend further studies on the band with that balance the impact on the estimator in terms of the variance and the bias. Further, an exponential ratio form of the proposed estimator was recommended to be studied and its properties be examined.

Published in American Journal of Theoretical and Applied Statistics (Volume 11, Issue 2)
DOI 10.11648/j.ajtas.20221102.12
Page(s) 75-82
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2022. Published by Science Publishing Group

Keywords

Auxiliary Information, Non-response, Bias, Mean Squared Error

References
[1] Biemer, P. P. (2013). Using level-of-effort paradata in non-response adjustments with application to field surveys.. Journal of the Royal Statistical Society: Series A (Statistics in Society), 176 (1), 147-168.
[2] Brick, J. M. (2017).. Responsive survey designs for reducing non-response bias. Journal of Official Statistics, 33 (3), 735-752.
[3] Fakhouri, T. H.-R. (Vital and health statistics. Series 2, Data evaluation and methods research,). An investigation of non-response bias and survey location variability in the 2017-2018 National Health and Nutrition Examination Survey.. 2020, (185), 1-36.
[4] Franco, C. L. (2019). Comparative study of confidence intervals for proportions in complex sample surveys. Journal of survey statistics and methodology, 7 (3), 334-364.
[5] Hussain, S. A. (2020). Estimated finite population distribution function with dual use of auxiliary information under non-response. PloS one,., 15 (12), e0243584.
[6] Johnson, T. P. (2012). Response rates and non-response errors in surveys. Jama, 307 (17), 1805-1806.
[7] Li, T. F. (2019).. Second-order statistics analysis and comparison between arithmetic and average geometric fusion: Application to multi-sensor target tracking. Information Fusion,, 51, 233-243.
[8] Mostafa, T. &. (2015). The impact of attrition and non-response in birth cohort studies: a need to incorporate missingness strategies. Longitudinal and Life Course Studies, 6 (2), 131-146.
[9] Riaz, S. A. (2020). On the generalized class of estimators for estimation of finite population mean in the presence of non-response problem. Journal of Prime Research in Mathematics, 16 (1), 52-63.
[10] Singh, A. V. (2019). Improved predictive estimators for finite population mean using the Searls technique. Journal of Statistics and Management Systems, 22 (8), 1555-1571.
[11] Singh, G. N. (2021). Efficient combination of various estimators in the presence of non-response. Communications in Statistics-Simulation and Computation, 50 (8), 2432-2466.
[12] Singh, P. S. (2018). Effect of measurement error and non-response on the estimation of population mean. Investigación Operacional,, 39 (1), 108-120.
[13] Singh, R., Kumar, M., Chaudhary, M. K., & Smarandache, F. (2009). Estimation of mean in presence of non response using exponential estimator. Infinite Study.
[14] Wang, H. &. (2021). Propensity score estimation using density ratio model under item non-response. arXiv preprint arXiv, 2104. 13469.
[15] Yadav, D. K. (2018). Estimated finite population mean using known coefficient of variation in the simultaneous presence of non-response and measurement errors under a double sampling scheme. Journal of Reliability and Statistical Studies, 51-66.
[16] Zhang, Q. (2020). Mean estimation of sensitive variables under measurement errors and non-response. (Doctoral dissertation, The University of North Carolina at Greensboro).
Cite This Article
  • APA Style

    Charles Wanyingi Nderitu, Herbert Imboga, Anthony Wanjoya. (2022). Estimation of Finite Population Mean Using Ratio Estimator Based on Known Median of Auxiliary Variable in the Presence of Non-Response. American Journal of Theoretical and Applied Statistics, 11(2), 75-82. https://doi.org/10.11648/j.ajtas.20221102.12

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    ACS Style

    Charles Wanyingi Nderitu; Herbert Imboga; Anthony Wanjoya. Estimation of Finite Population Mean Using Ratio Estimator Based on Known Median of Auxiliary Variable in the Presence of Non-Response. Am. J. Theor. Appl. Stat. 2022, 11(2), 75-82. doi: 10.11648/j.ajtas.20221102.12

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    AMA Style

    Charles Wanyingi Nderitu, Herbert Imboga, Anthony Wanjoya. Estimation of Finite Population Mean Using Ratio Estimator Based on Known Median of Auxiliary Variable in the Presence of Non-Response. Am J Theor Appl Stat. 2022;11(2):75-82. doi: 10.11648/j.ajtas.20221102.12

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  • @article{10.11648/j.ajtas.20221102.12,
      author = {Charles Wanyingi Nderitu and Herbert Imboga and Anthony Wanjoya},
      title = {Estimation of Finite Population Mean Using Ratio Estimator Based on Known Median of Auxiliary Variable in the Presence of Non-Response},
      journal = {American Journal of Theoretical and Applied Statistics},
      volume = {11},
      number = {2},
      pages = {75-82},
      doi = {10.11648/j.ajtas.20221102.12},
      url = {https://doi.org/10.11648/j.ajtas.20221102.12},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajtas.20221102.12},
      abstract = {The issue of non-response is a common phenomenon in sample surveys. Therefore, there is a need to develop ways of dealing with the challenge whenever it occurs. The current paper first introduces the stratification of the population as a result of the non-response. A theoretical review of the basic non response in sampling is as well explained and derived. The condition that leads to the first non-response estimator as proposed by the Hansen and Hurwitz. The resampling scheme for the non-response adjustment was described. This forms the bases for the new model which proposes a modified ratio estimator of the finite population mean in the presence of non-response when the population median of the auxiliary variable is known.  The properties of the proposed estimators are derived and theoretically compared with existing ones. A theoretical efficiency comparison shows that the proposed estimator performs better than the existing ones. Further, the simulated numerical comparison shows that the Bias of the proposed estimator performs better, while its Mean squared error is competitive. Towards, the conclusion of the study we recommend further studies on the band with that balance the impact on the estimator in terms of the variance and the bias. Further, an exponential ratio form of the proposed estimator was recommended to be studied and its properties be examined.},
     year = {2022}
    }
    

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    AU  - Charles Wanyingi Nderitu
    AU  - Herbert Imboga
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    DO  - 10.11648/j.ajtas.20221102.12
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    JF  - American Journal of Theoretical and Applied Statistics
    JO  - American Journal of Theoretical and Applied Statistics
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    AB  - The issue of non-response is a common phenomenon in sample surveys. Therefore, there is a need to develop ways of dealing with the challenge whenever it occurs. The current paper first introduces the stratification of the population as a result of the non-response. A theoretical review of the basic non response in sampling is as well explained and derived. The condition that leads to the first non-response estimator as proposed by the Hansen and Hurwitz. The resampling scheme for the non-response adjustment was described. This forms the bases for the new model which proposes a modified ratio estimator of the finite population mean in the presence of non-response when the population median of the auxiliary variable is known.  The properties of the proposed estimators are derived and theoretically compared with existing ones. A theoretical efficiency comparison shows that the proposed estimator performs better than the existing ones. Further, the simulated numerical comparison shows that the Bias of the proposed estimator performs better, while its Mean squared error is competitive. Towards, the conclusion of the study we recommend further studies on the band with that balance the impact on the estimator in terms of the variance and the bias. Further, an exponential ratio form of the proposed estimator was recommended to be studied and its properties be examined.
    VL  - 11
    IS  - 2
    ER  - 

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Author Information
  • Department of Statistics and Actuarial Sciences, Jomo Kenyatta University of Agriculture and Technology, Nairobi, Kenya

  • Department of Statistics and Actuarial Sciences, Jomo Kenyatta University of Agriculture and Technology, Nairobi, Kenya

  • Department of Statistics and Actuarial Sciences, Jomo Kenyatta University of Agriculture and Technology, Nairobi, Kenya

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