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Bhat, M. A.
- Variance Estimation Using Linear Combination of Tri-Mean and Quartiles
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Authors
Affiliations
1 Division of Agricultural Statistics, Sher-e-Kashmir University of Agricultural Science and Technology-Kashmir, Kashmir (J&K), IN
2 Division of Agricultural Statistics, Sher-e-Kashmir University of Agricultural Science and Technology-Kashmir, Kashmir(J&K), IN
1 Division of Agricultural Statistics, Sher-e-Kashmir University of Agricultural Science and Technology-Kashmir, Kashmir (J&K), IN
2 Division of Agricultural Statistics, Sher-e-Kashmir University of Agricultural Science and Technology-Kashmir, Kashmir(J&K), IN
Source
International Research Journal of Agricultural Economics and Statistics, Vol 8, No 1 (2017), Pagination: 143-145Abstract
In this paper, we have proposed a class of modified ratio type variance estimator for estimation of population variance of the study variable, when Tri-mean and Quartiles of the auxiliary variable are known. The bias and mean square error (MSE) of the proposed estimator are obtained. From the numerical study it is observed that the proposed estimator performs better than the existing estimators in the literature.Keywords
Simple Random Sampling, Bias, Mean Square Error, Tri-Mean, Quartiles, Auxiliary Variable.
References
- Cochran , W.G. (1977). Sampling Techniques. 3rd Ed., Wiley Eastern limted.
- Isaki, C.T. (1983). Variance estimation using auxiliary information. J. American Statistical Association,78 :117123.
- Kadilar, C. and Cingi, H. (2006). Improvement in Variance estimation using auxiliary information. Hacettepe J.Mathematics & Statistics, 35(1) : 117-115.
- Murthy, M. N. (1967). Sampling theory and methods. Calcutta Statistical Publishing House, India.
- Sumramani, J. and Kumarapandiyan, G. (2015).Generalized modified ratio type estimator for estimation of population variance. Sri-Lankan J. Appl. Statistics,16 (1) : 69-90.
- Wolter, K.M. (1985). Introduction to variance estimation. Springer- Verlag.
- Variance Estimation Using Linear Combination of Hodges-Lehmann and Quartiles
Abstract Views :184 |
PDF Views:0
Authors
Affiliations
1 Division of Agricultural Statistics, Sher-e-Kashmir University of Agricultural Science and Technology-Kashmir, Kashmir (J&K), IN
1 Division of Agricultural Statistics, Sher-e-Kashmir University of Agricultural Science and Technology-Kashmir, Kashmir (J&K), IN
Source
International Research Journal of Agricultural Economics and Statistics, Vol 8, No 1 (2017), Pagination: 183-185Abstract
In this paper, we have proposed a class of modified ratio type variance estimator for estimation of population variance of the study variable, when Hodges-Lehmann and Quartiles of the auxiliary variable are known. The bias and mean square error (MSE) of the proposed estimator are obtained. From the numerical study it is observed that the proposed estimator performs better than the existing estimators in the literature.Keywords
Simple Random Sampling, Bias, Mean Square Error, Hodges-Lehmann, Quartiles, Auxiliary Variable.References
- Cochran, W.G. (1977). Sampling Techniques. 3rd Ed., Wiley Eastern limted.
- Isaki, C.T. (1983). Variance estimation using auxiliary information. J. American Statist. Assoc.,78 :117-123.
- Kadilar, C. and Cingi, H. (2006). Improvement in Variance estimation using auxiliary information. Hacettepe J.Mathematics & Statist., 35(1) : 117-115.
- Murthy, M. N. (1967). Sampling theory and methods. Calcutta Statistical Publishing House, India.
- Sumramani, J. and Kumarapandiyan, G. (2015).Generalized modified ratio type estimator for estimation of population variance. Sri-Lankan J. Appl. Statist.,16 (1) : 69-90.
- Wolter, K.M. (1985). Introduction to variance estimation. Springer- Verlag.
- Variance Estimation Using Linear Combination of Non-Conventional Measures, Quartile Average and Deciles Mean
Abstract Views :162 |
PDF Views:0
Authors
Affiliations
1 Division of Agricultural Statistics, SKUAST, Kashmir (J&K), IN
1 Division of Agricultural Statistics, SKUAST, Kashmir (J&K), IN
Source
International Research Journal of Agricultural Economics and Statistics, Vol 8, No 2 (2017), Pagination: 431-434Abstract
Use of auxiliary information in survey sampling plays important role in getting more precision for estimating population parameters. Thus it has now become indispensable to use auxiliary information, thus in this paper we propose new modified ratio estimators by using the auxiliary information of non conventional location measures, non conventional measures of dispersion, quartile average, decile mean and their linear combinations for estimating population variance. The properties associated with proposed estimators are assessed by mean square error, bias and compared with existing estimators. By this comparison we conclude that our proposed estimators are more efficient than the existing estimators. To support the theoretical results, numerical study is provided.Keywords
Simple Random Sampling, Bias, Mean Square Error, Downtown’s Method, Deciles, Efficiency.References
- Cochran , W. G. (1977). Sampling techniques. 3rd Ed., Wiley Eastern Limted.
- Isaki, C.T. (1983). Variance estimation using auxiliary information. J. American Statist. Assoc.,78 :117-123.
- Kadilar, C. and Cingi, H. (2006). Improvement in variance estimation using auxiliary information. Hacettepe J. Mathematics & Statist., 35(1) : 117-115.
- Murthy, M. N. (1967). Sampling theory and methods. Calcutta Statistical Publishing House, India.
- Sumramani, J. and Kumarapandiyan, G. (2015).Generalized modified ratio type estimator for estimation of population variance. Sri-Lankan J. Appl. Statist.,16 (1): 69-90.
- Wolter, K.M. (1985). Introduction to variance estimation. Springer-Verlag.