We derive a new class of priors for the variance component in the Fay--Herriot model, a mixed regression model widely used in small area estimation. This class includes the well-known uniform or superharmonic prior. Through simulation we illustrate the use of our class of priors. Copyright 2008,...

For the well-known Fay-Herriot small area model, standard variance component estimation methods frequently produce zero estimates of the strictly positive model variance. As a consequence, an empirical best linear unbiased predictor of a small area mean, commonly used in small area estimation,...

A robust hierarchical Bayes method is developed to smooth small area means when a number of covariates are available. The method is particularly suited when one or more outliers are present in the data. It is well known that the regular Bayes estimators of small. area means, under normal prior...

A large number of networks in the real world have a scale-free structure, and the parameters of the networks change stochastically with time. Searching for the shortest paths in a scale-free dynamic and stochastic network is not only necessary for the estimation of the statistical...

This paper proposes an appropriate combinational approach which is based on improved BP neural network for short-term gas load forecasting, and the network is optimized by the real-coded genetic algorithm. Firstly, several kinds of modifications are carried out on the standard neural network to...