Sugarcane plays an essential role in the economy of the India. During 2018, 79.9% of total sugarcane production of India was used in the manufacture of white sugar, 11.29% was used for jaggery production, and 8.80% was used as seed and feed materials. 840.16 Mt sugarcane was exported in the year 2019. Prediction of production level is basic to effective decision-making for policymakers. The objective of this study is thus to find the suitable models of forecasting for sugarcane production. India and major sugarcane producing states, namely Andhra Pradesh, Karnataka, Maharashtra, Tamil Nadu and Uttar Pradesh were selected. Sugarcane production data from 1950 to 2015 were used for training and 2016 to 2018 was used to test the model. ARIMA method was used to model the production process. Order selection was done using AIC. RMSE, MAPE and Theils' U statistic were used to test the accuracy of the models fitted to the data. ARCH process was found for Karnataka, Tamil Nadu and Uttar Pradesh. Autocorrelation was not present in all the data series analyzed. Forecast accuracy on MAPE criteria ranged from 0.046 to 0.197 percent.
In this paper, a new discrete distribution called Binomial-Discrete Lindley (BDL) distribution is proposed by compounding the binomial and discrete Lindley distributions. Some properties of the distribution are discussed including the moment generating function, moments and hazard rate function. The estimation of distribution parameter is studied by methods of moments, proportions and maximum likelihood. A simulation study is performed to compare the performance of the di¤erent estimates in terms of bias and mean square errors. Automobile claim data applications are also presented to see that the new distribution is useful in modelling data.
In this study, a new family of distributions is introduced which is called alpha log-transformation family. We consider a special case of this family with exponential distribution in details. Several properties of the proposed distribution including the raw moments, moment generating function, quantile function and hazard rate function are obtained. Statistical inference is discussed based on complete and progressive censored samples. Simulation study is also performed to observe the performance of the estimates and approximate confidence intervals. A real data is given to illustrate the capability of ALT-Exponential distribution for modelling real data.
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