Small Area Estimation Based on Data Cloning Approach

Small Area Estimation Based on Data Cloning Approach

The frequentist analysis of complex models such as GAMM is computatioonally difficult. On the other hand, the advent of the Markov chain Monte Carlo algorithm has made the Bayesian analysis of complex models computationally convermient. RECEint introduction of the method of data cloning has made frequentist analysis of mixed models also equally computationally convenient. We propose the recently introduced approach of data cloning to conduct frequentist analysis of small area, estimation to overcome the problems which statisticians face in the context of small area, estimation. Another important feature of the proposed approach is to predict small area. parameters by providing prediction intervals. We illustrate the performance of the proposed approach through several simulation studies.