Small area estimation by splitting the sampling weights

Abstract

A new method is proposed for small area estimation. The principle is based upon the splitting of the sampling weights between the areas. A matrix of weights is defined. Each column of this matrix enables us to estimate the total of the variables of interest at the level of an area. This method automatically satisfies the coherence property between the local estimates and the overall estimate. Moreover, the local estimators are calibrated on auxiliary information available at the level of the small areas. This methodology also enables the use of composite estimators that are weighted means between a direct estimator and a synthetic estimator. Once the weights are computed, the estimates can be easily computed for any variable of interest. A set of simulations shows the interest of the proposed method.