'' '' '' '' Impact of Robust Estimators on Variance Estimation in Survey Sampling, Using Conventional and Non-Conventional Parameters as Auxiliary Information
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National Academy of Agricultural Sciences (NAAS)
NAAS Score: *5.38 (2020)
[Effective from January 1, 2020]
For more details click here
ICV 2019: 96.39
Index Copernicus ICI Journals Master List 2019 - IJCMAS--ICV 2019: 96.39
For more details click here
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Original Research Articles                      Volume : 7, Issue:10, October, 2018

PRINT ISSN : 2319-7692
Online ISSN : 2319-7706
Issues : 12 per year
Publisher : Excellent Publishers
Email : editorijcmas@gmail.com / submit@ijcmas.com
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Index Copernicus ICV 2018: 95.39
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Int.J.Curr.Microbiol.App.Sci.2018.7(10): 3358-3362
DOI: https://doi.org/10.20546/ijcmas.2018.710.389


Impact of Robust Estimators on Variance Estimation in Survey Sampling, Using Conventional and Non-Conventional Parameters as Auxiliary Information
M.A. Bhat*, T.A. Raja and S. Maqbool
Division of Agricultural Economics and Statist SKUAST-Kashmir (190025), India
*Corresponding author
Abstract:

In the present study, we have developed new estimators for the estimation of finite population variance by using auxiliary information as combination of conventional and non-conventional measures. Bias and mean square error has been worked out up to the first order of approximation. The empirical study has been carried out through numerical demonstration, under which improved estimators have performed better than the other existing estimators.


Keywords: Sample Random Sampling, Bias, MSE, Quartiles and efficiency
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How to cite this article:

Bhat, M.A., T.A. Raja and Maqbool, S. 2018. Impact of Robust Estimators on Variance Estimation in Survey Sampling, Using Conventional and Non-Conventional Parameters as Auxiliary InformationInt.J.Curr.Microbiol.App.Sci. 7(10): 3358-3362. doi: https://doi.org/10.20546/ijcmas.2018.710.389
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