Refine your search
Collections
Co-Authors
A B C D E F G H I J K L M N O P Q R S T U V W X Y Z All
Archana,
- Testing Statistical Models for forecasting Malaria Cases in India
Abstract Views :218 |
PDF Views:0
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
- Acharya, A.R., Magisetty, J.L., Chandra, V.R., Chaithra, B.S., Khanum, T. and Vijayan, V.A. (2013). Trend of malaria incidence in the state of Karnataka, India for 2001 to 2011. Archives Appl. Sci. Res., 5(3):104-111.
- Box, G.E. and Jenkins, G.M. (1976). Time series analysis forecasting and control. Holden- Day. San Fran., USA
- Cressie, N. (1988). A graphical procedure for determining nonstationary in time series. JASA, 83 (404) : 1108-1015.
- Das, N.G., Baruah, I., Kamal, S., Sarkar, P.K., Das, S.C. and Santhanam, K. (1997). An epidemiological and entomological investigation on malaria outbreak at Tamalpur PHC, Assam. Indian J. Malariol, 34 (3) : 164– 170.
- Dutta, P., Khan, A.M. and Mahanta, J. (1999). Problem of malaria in relation to socio-cultural diversity in some ethnic communities of Assam and Arunachal Pradesh. J.Parasitic Dis., 23 : 101–104.
- Kondrachine, A.V. (1992). Malaria in WHO Southeast Asia Region. Indian J. Malariol, 29 (3) : 129–160.
- Makridakis, S. and Hibbon, M. (1979). Accuracy of forecasting: An empirical investigation. J.Roy.Statist.Soc.A., 41(2): 97-145.
- NIMR. Estimation of True Malaria Burden in India. A Profile of National Institute of Malaria Research.
- Prakash, A., Mohapatra, P.K., Bhattacharyya, D.R., Doloi, P.
- 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 Generalized Class of Synthetic Estimator with Application to Estimation of Milk Production for Small Domains
Abstract Views :300 |
PDF Views:0
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.