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Iqbal Jeelani Bhat, M.
- 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
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- 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.
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- 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.
- A Generalized Class of Synthetic Estimator with Application to Estimation of Milk Production for Small Domains
Abstract Views :300 |
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Authors
Affiliations
1 Division of Statistics and Computer Science, SKUAST- J, Chatha (J&K), IN
2 Division of Statistics and Computer Science,SKUAST-J, Chatha (J&K), IN
1 Division of Statistics and Computer Science, SKUAST- J, Chatha (J&K), IN
2 Division of Statistics and Computer Science,SKUAST-J, Chatha (J&K), IN
Source
International Research Journal of Agricultural Economics and Statistics, Vol 10, No 1 (2019), Pagination: 115-121Abstract
The demand for small area statistics is growing day-by-day not only in public but also in private sectors, and small area estimation technique (SAE) is becoming very important in survey sampling due to the thrust of planning process has shifted from macro to micro level. Small area estimation is one of the several techniques which involves the estimation of parameters for small subpopulation generally used when the sub-population of interest is included in a larger survey. In this article the proposed class of synthetic estimators gives consistent estimators if the synthetic assumption holds. Further it demonstrates the use of the generalized synthetic and ratio synthetic estimators for estimating the milk production for small domains, empirically through a real data set.Keywords
Synthetic Estimator, Small Area Estimation, Small Area.References
- Brackstone, G.J. (1987). Small area data: Policy issues and technical challenges, In : R. Platek, J.N.K. Rao, C.E. Sarndal and M.P. Singh (Edition), Small area statistics, John Wiley and Sons, New York, U.S.A., pp.3-20.
- Ghosh, M. and Rao, J.N.K. (1994). Small area estimation: an appraisal (with discussion). Statistical Sci., 9: 65-93.
- Gonzales, M.E. (1973). Use and evaluation of synthetic estimators, Proceedings of the Social Statistics Section of the American Statistical Association, 33-36pp.
- Pandey, Krishan K. (2011). Generalized class of synthetic estimators for small area under systematic sampling design. Statistics in Transition- New Series, Poland, 11 (1) 75-89.
- Tikkiwal, G. C. and Ghiya, A. (2000). A generalized class of synthetic estimators with application to crop acreage estimation for small domains. Biometrical J., 42 (7) : 865876.