  A two-stage normal hierarchical model called the Fay--Herriot model and the
empirical Bayes estimator are widely used to provide indirect and model-based
estimates of means in small areas. However, the performance of the empirical
Bayes estimator might be poor when the assumed normal distribution is
misspecified. In this article, we propose a simple modification by using
density power divergence and suggest a new robust empirical Bayes small area
estimator. The mean squared error and estimated mean squared error of the
proposed estimator are derived based on the asymptotic properties of the robust
estimator of the model parameters. We investigate the numerical performance of
the proposed method through simulations and an application to survey data.
