Small area estimation

Small area estimation techniques with survey data we examined. On-the-ground inventories could collect very intensive but costly grid information. Therefore, utilizing the low-cost auxiliary information from remote sensing sources such as Landsat Thematic Mapper or topographical information is preferred. Since the data is spatially dependent, it is crucial to develop procedures that combine the existing small area methods with spatial models. The following methods are proposed using transformed and untransformed data: spatial multivariate distributions on transformed data, spatial zero-inflated exponential (SZIE) models, spatial zero-inflated gamma (SZIG) models, spatial zero-inflated Poisson (SZIP) models and spatial zero-inflated negative binomial (SZINB) models. The ancillary data is incorporated into simple spatial multivariate models, called spatial multivariate regression models. The comparison of the result from spatial multivariate regression models with linear regression models and the Most Similar Neighbor (MSN) procedure was provided. Prediction for individual plot locations on a 0.85mile grid using 1.7-mile grid data were generated and several simulations yielding realizations similar to the available data were investigated to see the reliability of prediction errors.;For spatial models without dealing with numerous zeros in our data, simple spatial multivariate model are considered for transformed data. Such models can only make predictions for plots on the 1.7-mile grid. Best linear unbiased predictions (BLUP) are designed to make predictions for plots on shorter distance grids. BLUP based on untransformed and transformed data for non-sampled sites are given. The performance of different models in this study have been compared. Spatial multivariate regression models incorporating auxiliary information did not show improvement based on reduced mean-squared errors.