Spatial Bootstrap Variance Estimation Method for Missing Survey Data

Abstract

In this study, an attempt was made to develop bootstrap variance estimation procedure for Spatial Estimator (SE) of finite population mean in presence of missing observations under Simple Random Sampling Without Replacement. The Proportional Spatial Bootstrap (PSB) method has been proposed considering spatial relationship between sampling units. Under this technique, different spatial imputation techniques based on the spatial dependency of data were used to impute missing observations in the observed sample. The statistical properties of the proposed PSB techniques were studied empirically through a simulation study. The simulation results reveal that using appropriate spatial data-dependent imputation techniques, the proposed PSB technique performed better than its existing techniques available in the literature.