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Kumar, Banti
- 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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- Robust Modeling in the Presence of Outliers for Food Grain Production in India
Abstract Views :217 |
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Authors
Affiliations
1 Division of Statistics and Computer Science, Sher-e-Kashmir University of Agricultural Sciences and Technology of Jammu, Jammu (J&K), IN
1 Division of Statistics and Computer Science, 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 9, No 1 (2018), Pagination: 25-30Abstract
The traditional ordinary least squares procedure (OLS) is the most frequently used method for analyzing food grain production data (1983-2014), but ignore the presence of outliers or influential data points which may distort the regression estimates obtained from OLS. These data points may remain unnoticed and can have a strong adverse affect on the regression estimates. In this paper, two approaches i.e., robust M-regression and quantile regression to linear robust regression analysis are presented, as these methods provide formal procedure to overcome from the situation of outliers and influential observations and to reduce their influence on the final estimates of the regression co-efficients by using Cobb-Douglas production function. Moreover, 0.90th quantile regression model comes out to be best on the basis of AIC (-47.17), SBIC (-36.91), elasticity of production, marginal value productivity, sign, size and the variables significant effect on foodgrain production than OLS and robust M-regression. Also, the variables NSA and AC were best in order to increase the food grain production on the basis of quantile 0.90th regression, elasticity of production and MVP at 0.90th quantile.Keywords
Ordinary Least Square, Outliers, Robust Regression, Quantile Regression, M-Estimator, Food Grain Production.References
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