Tauqueer AhmadPramod Kumar Moury2021-04-062021-04-062020https://krishikosh.egranth.ac.in/handle/1/5810163355T-10423In many surveys (e.g. agriculture, forestry, environmental, ecological surveys etc.), observations are often spatially correlated, thus, the relationship between dependent and independent variables varies across the locations in the study area. The commonly used survey estimation methods ignore spatial non-stationarity present in the population data. The sample survey data often contains outliers which lead to an increase in bias and variance of estimator of finite population total. In view of the abovementioned problem of estimation, outlier robust estimators of finite population total have been proposed in this study which are based on Geographically Weighted Regression (GWR) model to capture the spatial non-stationarity present in the survey data. This method is also robust against the presence of outliers in survey data. The proposed Outlier Robust Geographically Weighted Regression (ORGWR) estimators have been developed under the five different shapes of spatial weighting functions (i.e. bi-square, boxcar, exponential, gaussian, tri-cube). Based on the simulation study, some of the proposed ORGWR estimators have been found better as compared to a non-robust GWR estimator as well as a robust estimator proposed by Chamber (1986). For estimation of variance of non-robust GWR estimator of finite population total, three bootstrap techniques of variance estimation i.e. Spatial Rescaled Residual Bootstrap (SRRB) method, Spatial Centered Residual Bootstrap (SCRB) method and Spatial Pair Bootstrap (SPB) method have been proposed. Based on the simulation study, the SRRB method has been found to be the best among all the proposed bootstrap procedures. Further, Spatial Weighted Residual Bootstrap (SWRB) method of variance estimation technique for ORGWR estimator under the five different shapes of spatial weighting functions (i.e. bi-square, boxcar, exponential, gaussian, tri-cube) have been proposed. Based on the simulation study, the SWRB method with bi-square shape of spatial weighting function has been found to be the best performing bootstrap procedure of variance estimation for ORGWR estimator. Further, a Proportional Spatial Weighted Residual Bootstrap (PSWRB) technique to estimate the variance of ORGWR estimator in the presence of missing observations in the survey data set has been proposed. GWR model-based imputation has been found to be the most efficient imputation technique in estimating the variance using the PSWRB technique in the presence of missing observations in survey data. The proposed methodology (ORGWR) has also been applied to the real data set and application of the developed methodology has resulted in obtaining reliable estimates of average yield of cotton with significantly reduced number of CCEs.EnglishThesis