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Not AvailableGeneral Crop Estimation Surveys (GCES) Scheme is adopted in all the states of country to estimate crop yield at higher level (state, district). With progress in planning in agriculture, especially in case of the cotton crop, we need estimate of cotton crop yield at tehsil/block level with the desired degree of precision. Application of GCES as such, as tehsil/block level with the same number of crop cutting experiments (CCEs) may yield estimates with less degree of precision. If the simple crop-cutting approach is to be adopted directly for this purpose, the present number of crop cutting experiments will have to be increased significantly. In such case, use of information getting from auxiliary variable correlated with variable under study may increase the degree of precision of estimates at tehsil level. In this study, double sampling regression approach under stratified two stage sampling design framework has been proposed for estimation of the average yield of cotton at the tehsil level using the picking having highest correlation with total yield as auxiliary variable.Not Availabl

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Not AvailableIn a Dual Frame (DF) surveys, set of two frames is used instead of a traditional single frame of sampling units from the target population. Dual frame surveys are applicable in those situations where one frame covers the entire population but very expensive to sample; so an alternate frame may be available that does not cover the entire population but is inexpensive to sample. As Hartley (1962) noted, variance estimation can be more complicated for dual frame surveys than for a single-frame survey. Unbiased variance estimator of parameter of interest is very tedious to obtain for estimator using dual frame surveys. In this article, we propose two rescaling bootstrap variance estimation techniques in dual frame surveys viz. Stratified Rescaling Bootstrap Without Replacement (SRBWO) and Post-stratified Rescaling Bootstrap Without Replacement (PRBWO) methods. Statistical properties of the proposed methods are compared through a simulation study. Simulation results suggest that the proposed SRBWO and PRBWO methods give an unbiased estimate of the variance of the dual frame estimator of population total and the SRBWO method performs better than the PRBWO method.Not Availabl

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Not AvailableWhen survey data shows spatial non-stationarity then geographically weighted regression (GWR) approach explains the data more effectively than standard global regression model. In this article, two outlier robust geographically weighted regression (RGWR) estimators have been proposed to estimate the finite population total under spatial nonstationarity. The first RGWR estimator is based on winsorization whereas second one is based on filtering of outliers. In order to compare the statistical performance of proposed estimators with standard non-robust GWR estimator and a robust estimator proposed by Chamber (1986), a simulation study was carried out. It has been observed that proposed estimator based on winsorization of sampled data performs fairly well in a scenario where spatial non-stationarity appears in population and the survey data contains outliersNot Availabl