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This paper attempts to compare some transformed ratio type estimators for estimating the population mean ... of the study variable y using information on the auxiliary variable x in the presence of non-response. A generalized transformed ratio type estimator has been proposed and its properties are discussed. Various special cases of this estimator are considered. An empirical study has been conducted with real populations to study the performance of the estimators. It has been found that the estimators due to Sisodia and Dwivedi (1981) and Upadhyay and Singh (1999) have over performed other estimators in presence of non-response. However, for optimum choice of constants the proposed estimator along with estimator due to Khare and Srivastava (1997) and regression estimators are equally efficient and over preformed other estimators. [ABSTRACT FROM AUTHOR]

In the present paper, an attempt has been made to develop two-steps calibration estimator of population mean in two-stage stratified random sampling, first by calibrating sampling design weight at psu level and second by calibrating stratum weight using known total of the auxiliary information at population level. A limited simulation study with real data has been conducted to examine the relative performance of the calibration estimators over the usual estimator of the population mean without using auxiliary information in two-stage stratified random sampling. Results of the simulation study show that two-steps calibration estimator has brought considerable improvement in the precision of the estimate of the population mean. [ABSTRACT FROM AUTHOR]

Various statistical models to study the effect of weather variables on rapeseed & mustard yield at different stages of crop and to forecast its yield for Faizabad District have been attempted. Time series data on yield for rapeseed & mustard crop of Faizabad district of Uttar Pradesh for 22 years (1990-91 to 2011-12) have been used. Weekly weather data for the period (1991-2012) on six weather variables viz. Minimum Temperature, Maximum Temperature, Relative Humidity at 7 hour, Relative Humidity at 14 hour, Sun-shine hours and Wind-Velocity of Faizabad district have been utilised for the present study. Various models have been tried to know the relationship between rapeseed & mustard crop yield by generating new weather indices from weekly data. In all, eight statistical models were developed. The model involving total of value of weather variable over weeks and linear functions of respective correlation coefficients between adjusted crop yield for trend effect and weekly data and weather variables as the independent variables has been found to be the best one (model VII & VIII) for studying the effect of individual weather variables. A forecast model has been developed to forecast the pre-harvest crop yield. The results revealed that forecast at 21st week of the crop period i.e. during third week of February before one and half months of its harvest was found to be reliable one as percent standard error of forecast was obtained to be approximately 4 percent. [ABSTRACT FROM AUTHOR]

Calibration estimation is a popular technique for increasing the precision of estimates of population parameters by using specified auxiliary information. Some ratio type estimators have been considered for estimating population mean by making use of a set of calibration constraints in stratified sampling. The estimator of variance of the proposed calibration estimator of mean is derived using a lower level calibration approach. Simulation study is used to evaluate the performances of the proposed estimators by comparing them with the usual ratio type estimators. [ABSTRACT FROM AUTHOR]

Deville and Särndal (1992) developed calibration estimator by using the auxiliary information to obtain a better estimate of the population total of study variate y. While calibration approach does not assume any explicit relationship between y and x but it assumes that only X, the population total of x is known. Prediction approach advocated by Royall and Herson (1973) leads to model based estimator of finite population under a assumption of specified super population model where xi's are supposed to be known for all i = 1, 2, 3, ..., N. Wu and Sitter (2001) proposed a design oriented model based calibration estimator of population total, when they also assumed that Xi are known for all i = 1, 2, 3, ..., N. It has been shown that these three approaches i.e. calibration approach, model based approach and model based calibration approach provides the same estimator under some situation. However, their variances are different. In the present paper, an attempt has been made to conduct a limited simulation study to examine the relative performance of the estimators based on the aforesaid three approaches. From the results of the simulation study, it has been found that calibration estimates are best. [ABSTRACT FROM AUTHOR]

Application of principal component analysis in developing statistical models for forecasting pigeon-pea yield has been demonstrated. The time series data on pigeon-pea yield and weekly data on eight weather variables viz., Minimum and maximum temperature ,Rainfall and number of Rainy days, Relative Humidity at 7hr and 14hr, Sun-Shine hours and Wind- Velocity pertaining to the period 1990-91 to 2011-12 in Faizabad district of Uttar Pradesh have been used in this study. Weather indices have been constructed using weekly data on weather variables [Agrawal et al. (1983)]. Total six models have been developed using principal component analysis of weather indices. The forecast yields have been found to be very close to the actual yield except in one or two cases. The per cent standard errors of forecast yield obtained from the model-2 and 5 have been found below 5.50 per cent in comparison to other models. Therefore, the model-2 and 5 have been found to be most appropriate on the basis of values of R2, per cent deviation of forecast, per cent root mean error (%RMSE) and per cent standard error (PSE) for the reliable forecast of pigeon-pea yield two and half months before the harvest of the crop. [ABSTRACT FROM AUTHOR]