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Raja, T. A.
- On Generalized Logarithmic Series Distribution and its Application to Leaf Spot Grades in Mulberry
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Authors
Affiliations
1 Division of Agricultural Statistics, Sher-e-Kashmir University of Agricultural Sciences and Technology (K), Srinagar (J&K), IN
2 Division of Animal Genetics and Breeding, Sher-e-Kashmir University of Agricultural Sciences and Technology (K), Suhuma (J&K), IN
3 Division of Agricultural Economics and Statistics, Sher-e-Kashmir University of Agricultural Sciences and Technology (K), Wadura (J&K), IN
4 Division of Agricultural Statistics, Shalimar, Sher-e-Kashmir University of Agricultural Science and Technology (K), Srinagar (J&K), IN
5 Division of Statistics and Computer Science, Sher-e-Kashmir University of Agricultural Sciences and Technology (K), Chatha (J&K), IN
1 Division of Agricultural Statistics, Sher-e-Kashmir University of Agricultural Sciences and Technology (K), Srinagar (J&K), IN
2 Division of Animal Genetics and Breeding, Sher-e-Kashmir University of Agricultural Sciences and Technology (K), Suhuma (J&K), IN
3 Division of Agricultural Economics and Statistics, Sher-e-Kashmir University of Agricultural Sciences and Technology (K), Wadura (J&K), IN
4 Division of Agricultural Statistics, Shalimar, Sher-e-Kashmir University of Agricultural Science and Technology (K), Srinagar (J&K), IN
5 Division of Statistics and Computer Science, Sher-e-Kashmir University of Agricultural Sciences and Technology (K), Chatha (J&K), IN
Source
International Research Journal of Agricultural Economics and Statistics, Vol 8, No 1 (2017), Pagination: 60-64Abstract
The generalized logarithmic series distribution (GLSD) adds an extra parameter to the usual logarithmic series distribution and was introduced by Jain and Gupta (1973). This distribution has found applications in various fields. The estimation of parameters of generalized logarithmic series distribution was studied by the methods of maximum likelihood, moments, minimum chi-square and weighted discrepancies. The GLSD was fitted to different leaf spot grades in four varieties of mulberry namely Ichinose, Gosherami, Rokokuvoso and Kokuso-20 and it was observed that all the estimation techniques perform well in estimating the parameters of generalized logarithmic series distribution but with varying degree of non-significance.Keywords
Generalized Logarithmic Series Distribution, Leaf Spot Grades, Mulberry.
References
- Consul, P.C. and Shenton, L.R. (1972). Use of Lagrangian expression for generating generalized probability distributions.SIAM J. Appl. Math., 23(2): 239-249
- Gupta, R.C. (1974). Modified power series distribution and some of its applications, Sankhya. Ser. B., 35: 288-298.
- Hanseen, B.B. and Willekens, E. (1990). The generalized logarithmic series distribution. Statistics & Probability Letters, 9 : 311-316.
- Jain, G.C. and Gupta, R.P. (1973). A logarithmic type distribution. Trabjos Estadist, 24 : 99 -105.
- Jani, P.N. (1977). Minimum variance unbiased estimate for some left truncated modified power series distribution.Sankhya, Series B., 3(39): 258-278.
- Jani, P.N. and Shah, S.M. (1979). On fitting of the generalized logarithmic series distribution. J. Indian Statistical Association, 30(3) : 1-10.
- Kemp, A.W. (1986). Weighted discrepancies and maximum likelihood estimation for discrete distributions.
- Communication in Statistics - Theory & Methods, 15(3): 783-803.
- Mishra, A. (1979). Generalization of some discrete distributions.. J. Bihar. Math. Soc., 11: 12-22.
- Rao, B.R. (1981). Correlation between the numbers of two types of children in a family with the mpsd for the family size.Communications in Statistics – Theory & Methods, 10(3): 249-254.
- Variance Estimation Using Linear Combination of Tri-Mean and Quartiles
Abstract Views :178 |
PDF Views:0
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 :180 |
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.