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Sharma, Manish Kr.
- Testing Statistical Models for forecasting Malaria Cases in India
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Authors
Affiliations
1 Sher-e-Kashmir University of Agricultural Sciences and Technology of Jammu, Jammu (J&K), IN
1 Sher-e-Kashmir University of Agricultural Sciences and Technology of Jammu, Jammu (J&K), IN
Source
International Research Journal of Agricultural Economics and Statistics, Vol 8, No 1 (2017), Pagination: 8-14Abstract
Malaria is still a big problem for a country like India especially with a huge number of slums and poor people having substandard living habits. The present study was conducted on the basis of secondary data available for malaria cases for the period of 1995 to 2011 to find out the trend for number of malaria cases in India and to forecast such cases for future periods. A number of time series models were created from the available data using the SAS software like linear trend, random walk with drift, simple exponential smoothing, log linear and finally the ARIMA models. The most suitable model was found to be the Log linear model with minimum MSE, RMSE and MSPE of 114402.9, 144675.8 and 5.59744, respectively. The forecast for number of malaria cases in India shown a decrease trend from 1122324 cases in the year 2015 to 778868 in the year 2023.Keywords
Malaria, ARIMA, ACF, PACF, Log Linear Model, AIC, SBIC.
References
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- NIMR. Estimation of True Malaria Burden in India. A Profile of National Institute of Malaria Research.
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- and Mahanta, J. (1997). Changing malaria endemicity—a village based study in Sonitpur, Assam. J. Commun. Dis., 29 (2) : 175–178.
- Sinton, J.A. (1935).What malaria costs India. Malaria Bureau 13. Govt. of India Press Delhi. Health Bull 1935; 26.
- WEBLIOGRAPHY
- Central Bureau of Health Intelligence (2010). Human Resources in Health Sector. Ministry of Health & Welfare, GOI. http:/ /cbhidghs.nic.in/., accessed on 29th July 2012.
- Central Bureau of Health Intelligence (2012). Health Status Indicators, National Health Profile. Ministry of Health & Welfare, GOI. http://cbhidghs.nic.in/., accessed on 29th July 2012.
- A New Approach of Ratio Estimation in Sample Surveys
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Authors
Affiliations
1 Sher-e-Kashmir University of Agricultural Sciences and Technology of Jammu, Jammu (J&K), IN
1 Sher-e-Kashmir University of Agricultural Sciences and Technology of Jammu, Jammu (J&K), IN
Source
International Research Journal of Agricultural Economics and Statistics, Vol 8, No 1 (2017), Pagination: 100-103Abstract
This article deals with the estimation of population mean under simple random sampling using a new form of ratio estimator. The expression for mean square error and bias has been obtained. An efficiency comparison is considered for proposed estimator with the classical ratio, product and exponential ratio estimator. Finally an empirical study is also carried out to judge the performance of proposed estimator.Keywords
Simple Random Sampling, Ratio Estimator, Mean Square Error, Efficiency, AMS Classification: 62D05.References
- Bahl, S. and Tuteja, R.K. (1991). Ratio and product exponential estimator. Information & Optimazimation Sci., 12 (1) : 159-163
- Cochran, W.G. (1997). Sampling Techniques, 3rd Ed., John Wiley & Sons, Inc., New York, U.S.A.
- Jeelani, M.I. and Maqbool, S. (2013). Modified ratio estimators of population mean using linear combination of coefficient of skewness and quartile deviation. South Pacific J. Nat. & Appl. Sci., 31 (1) : 39-44.
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- Singh, H.P. and Tailor, R. (2003). Use of known correlation Coefficient in estimating the finite population means, Statistics Transition, 6(4) : 555-560
- Singh, H.P., Singh, P., Tailor, R. and Kakran, M.S. (2004). An Improved Estimator of population mean using Power transformation. J. Indian Soc. Agric. Stat., 58(2) : 223-230.
- Singh, H.P. and Tailor, R. (2005). Estimation of finite population mean with known co-efficient of variation of an auxiliary, STATISTICA, anno 65 (3) : 301-313
- Upadhyaya, L.N. and Singh, H.P. (1999). Use of transformed auxiliary variable in estimating the finite population mean. Biometrical J., 41(5) : 627-636
- Yan, Z. and Tian, B. (2010).Ratio method to the mean estimation using co-efficient of skewness of auxiliary variable, ICICA2010, PartII,CCIS106(2010):103–110.
- Validation of two Parameter Function Height Diameter Models
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Authors
Affiliations
1 Sher-e-Kashmir University of Agricultural Sciences and Technology of Jammu, Jammu (J&K), IN
2 Sher-e-Kashmir University of Agricultural Sciences and Technology of Kashmir, Kashmir (J&K), IN
1 Sher-e-Kashmir University of Agricultural Sciences and Technology of Jammu, Jammu (J&K), IN
2 Sher-e-Kashmir University of Agricultural Sciences and Technology of Kashmir, Kashmir (J&K), IN
Source
International Research Journal of Agricultural Economics and Statistics, Vol 9, No 2 (2018), Pagination: 331-334Abstract
Eleven nonlinear height diameter models were fitted and developed for Pinus trees based on individual tree height and diameter at breast height data (n=300) collected from block Langate of Kashmir province in India. Fitting of height diameter models using non-linear least square regression showed that all the parameters across all models were significant. In order to test the predictive performance of the models 10- folded cross-validation technique was used in this study. Comparison of AIC, RMSE, ME and Ad-R2 values for the training and validation data showed that most of the non-linear HD models capture the height diameter relationships for Pinus trees. Validation results suggest that Naslund -2 HD model provide the best height predictions in case of Pinus tree.Keywords
Height, Diameter, Cross Validation, Pinus.References
- Colbert, K.C., Larsen, D.R. and Lootens, J.R. (2002). Heightdiameter equations for thirteen Midwestern bottomland hardwood species. Northern J. Appl. Forestry, 19: 171– 176.
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- Diamantopoulou, M.J. and Ozcelik, R. (2012). Evaluation of different modelling approaches for total tree-height estimation in Mediterranean region of Turkey.Forest Systems, 21: 383–397.
- Huang, S., Titus, S.J. and Wiens, D.P. (1992). Comparison of nonlinear height– diameter functions for major Alberta tree species. Can. J. For. Res., 22 : 1297– 1304.
- Jeelani, M.I., Mir, S.A., Khan, I.,Nazir, N. and Jeelani, F. (2015). Rank set sampling in improving the estimates of simple regression model. Pakistan J. Statistics & Operation Res., 11 (1) : 39-49.
- Larson, S. (1931). The shrinkage of the co-efficient of multiple correlation. J. Educ. Psychol., 22(1) : 45–55.
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- Menten, L. and Michaelis, M.I. (1913). Die kinetik der invertinwirkung. Biochem. Z., 49 : 333–369.
- Meyer, W. (1940). A mathematical expression for height curves. J. Forestry, 38 : 415 – 420.
- Mosteller, F. and Turkey, J.W. (1968).Data analysis, including statistics. In Handbook of Social Psychology. AddisonWesley, pp. 601–720.
- Naslund, M. (1937). Skogsforsoksanstaltens gallringsforsok i tallskog (Forest research institute’s thinning experiments in Scots pine forests). Meddelanden frstatens skogsforsoksanstalt Hafte 29. In Swedish.
- Olson, L.D. and Delen, D. (2008). Advanced data mining technique. Berli, Heidelberg, Springer: 180.
- Parresol, B.R. (1992). Bald cypress height-diameter equations and their prediction confidence interval. Canadian J. Forest. Res., 22: 1429–1434.
- Wani, F. J., Sharma, M.K., Rizvi, S.E.H and Jeelani, M.I. (2017). Predictive modelling and validation for estimating fodder yield ofGrewia optiva.Malaysion J.Sci., 36 (2):103-115.
- Wani, F. J., Sharma, M.K., Rizvi, S.E.H and Jeelani, M.I. (2018). A study on cross validation for model selection and Estimation. Internat. J. Agric. Sci., 14(1) : 165-172.
- Wykoff, W.R., Crookston, N.L. and Stage, A.R. (1982). User’s guide to the stand prognosis model. USDA For. Serv. Gen. Tech. Rep. 133.
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- A Generalized Class of Synthetic Estimator with Application to Estimation of Milk Production for Small Domains
Abstract Views :297 |
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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.