Hierarchical Bayes Aggregated Level Spatial Model for Crop Yield Estimation

摘要

The demand for acceptable disaggregated level statistics from sample surveys has grown substantially over the past decades due to their extensive and varied use in public and private sectors. Basically, it is the main endeavor of ‘ Small area estimation (SAE) ’ approach to produce sound prediction of a target statistic for small domains to answer the problem of small sample sizes. The traditional survey estimation approaches are not suitable enough for generating disaggregate or small domain levelestimates because of sample size problem. The SAE techniques therefore provide a feasible way to produce the reliable estimates at disaggregate level from the existing survey data.This paper explores a spatial dependent aggregated level Hierarchical Bayes (HB) model for SAE to estimate the yield for paddy (green) crop at district level in the state of Uttar Pradesh in India. The approach uses survey data from the Improvement of crop statistics (ICS) scheme collected by National Sample Survey Office (NSSO) and linked with Population Census. A considerable gain has been obtained while exploiting spatial information in aggregated level small area model.