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An Alternative Estimator for Estimating the Finite Population Mean in Presence of Measurement Errors with the View to Financial Modelling

Received: 5 November 2014     Accepted: 16 November 2014     Published: 18 November 2014
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Abstract

This article presents the problem of estimating the population mean using auxiliary information in the presence of measurement errors. We have compared the three proposed estimators being the exponential ratio-type estimator, Solanki et al. (2012) estimator, and the mean per unit estimator in the presence of measurement errors. Financial Model by Gujrati and Sangeetha (2007) has been employed in our empirical analysis. In that, our investigation has indicated that our proposed general class of estimator t4 is the most suitable estimator with a smaller MSE relative to other estimators under measurement errors.

Published in Science Journal of Applied Mathematics and Statistics (Volume 2, Issue 6)
DOI 10.11648/j.sjams.20140206.11
Page(s) 107-111
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), 2014. Published by Science Publishing Group

Keywords

Population Mean, Study Variate, Auxiliary Variates, Mean Squared Error, Measurement Errors, Efficiency, Financial Model

References
[1] Allen, J., Singh, H. P. and Smarandache, F. (2003): A family of estimators of population mean using multiauxiliary information in presence of measurement errors. International Journal of Social Economics 30(7), 837–849.
[2] Bahl, S. and Tuteja, R. K. (1991): Ratio and product type exponential estimator. Information and optimization sciences12 (1), 159-163.
[3] Gujarati, D. N. and Sangeetha (2007): Basic econometrics. Tata McGraw – Hill.
[4] Koyuncu, N. and Kadilar, C. (2010): On the family of estimators of population mean in stratified sampling. Pakistan Journal of Statistics (to be published).
[5] Kumar, M., Singh, R., Sawan, N. and Chauhan, P. (2011a): Exponential ratio method of estimators in the presence of measurement errors. Int. J. Agricult. Stat. Sci. 7(2): 457-461.
[6] Kumar, M., Singh, R., Singh, A.K. and Smarandache, F. (2011b): Some ratio type estimators under measurement errors. WASJ 14(2) :272-276.
[7] Manisha and Singh, R. K. (2001): An estimation of population mean in the presence of measurement errors. Journal of Indian Society of Agricultural Statistics 54(1), 13–18.
[8] Manisha and Singh, R. K. (2002): Role of regression estimator involving measurement errors. Brazilian journal of probability Statistics 16, 39- 46.
[9] Shalabh (1997): Ratio method of estimation in the presence of measurement errors. Journal of Indian Society of Agricultural Statistics 50(2):150– 155.
[10] Singh, H. P. and Karpe, N. (2008): Ratio-product estimator for population mean in presence of measurement errors. Journal of Applied Statistical Sciences 16, 49–64.
[11] Singh, H. P. and Karpe, N. (2009): On the estimation of ratio and product of two populations means using supplementary information in presence of measurement errors. Department of Statistics, University of Bologna, 69(1), 27-47.
[12] Singh, R., Kumar, M., Chaudhary, M.K. (2011): Improved Family of Estimators of Population Mean in Simple Random Sampling. WASJ 13(10), 2131-2136.
[13] Solanki, R.S., Singh, H. P. and Rathour, A. (2012): An alternative estimator for estimating the finite population mean using auxiliary information in sample surveys. ISRN Probability and Statistics doi:10.5402/2012/657682
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  • APA Style

    Rajesh Singh, Sachin Malik, Mohd Khoshnevisan. (2014). An Alternative Estimator for Estimating the Finite Population Mean in Presence of Measurement Errors with the View to Financial Modelling. Science Journal of Applied Mathematics and Statistics, 2(6), 107-111. https://doi.org/10.11648/j.sjams.20140206.11

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    ACS Style

    Rajesh Singh; Sachin Malik; Mohd Khoshnevisan. An Alternative Estimator for Estimating the Finite Population Mean in Presence of Measurement Errors with the View to Financial Modelling. Sci. J. Appl. Math. Stat. 2014, 2(6), 107-111. doi: 10.11648/j.sjams.20140206.11

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    AMA Style

    Rajesh Singh, Sachin Malik, Mohd Khoshnevisan. An Alternative Estimator for Estimating the Finite Population Mean in Presence of Measurement Errors with the View to Financial Modelling. Sci J Appl Math Stat. 2014;2(6):107-111. doi: 10.11648/j.sjams.20140206.11

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  • @article{10.11648/j.sjams.20140206.11,
      author = {Rajesh Singh and Sachin Malik and Mohd Khoshnevisan},
      title = {An Alternative Estimator for Estimating the Finite Population Mean in Presence of Measurement Errors with the View to Financial Modelling},
      journal = {Science Journal of Applied Mathematics and Statistics},
      volume = {2},
      number = {6},
      pages = {107-111},
      doi = {10.11648/j.sjams.20140206.11},
      url = {https://doi.org/10.11648/j.sjams.20140206.11},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.sjams.20140206.11},
      abstract = {This article presents the problem of estimating the population mean using auxiliary information in the presence of measurement errors. We have compared the three proposed estimators being the exponential ratio-type estimator, Solanki et al. (2012) estimator, and the mean per unit estimator in the presence of measurement errors. Financial Model by Gujrati and Sangeetha (2007) has been employed in our empirical analysis. In that, our investigation has indicated that our proposed general class of estimator t4 is the most suitable estimator with a smaller MSE relative to other estimators under measurement errors.},
     year = {2014}
    }
    

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    AB  - This article presents the problem of estimating the population mean using auxiliary information in the presence of measurement errors. We have compared the three proposed estimators being the exponential ratio-type estimator, Solanki et al. (2012) estimator, and the mean per unit estimator in the presence of measurement errors. Financial Model by Gujrati and Sangeetha (2007) has been employed in our empirical analysis. In that, our investigation has indicated that our proposed general class of estimator t4 is the most suitable estimator with a smaller MSE relative to other estimators under measurement errors.
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Author Information
  • Department of Statistics, BHU, Varanasi, India

  • Department of Statistics, BHU, Varanasi, India

  • Associate Professor of Finance, Ajman University of Science and Technology, Ajman, UAE

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