This paper introduces Information Theoretic - based methods for estimating a target variable in a set of small geographical areas, by exploring spatially heterogeneous relationships at the disaggregate level. Controlling for spatial effects means introducing models whereby the assumption is that...

This paper introduces Information Theoretic - based methods for estimating a target variable in a set of small geographical areas, by exploring spatially heterogeneous relationships at the disaggregate level. Controlling for spatial effects means introducing models whereby the assumption is that...

We develop a new way to incorporate prior information within an Information-Theoretic (IT) estimation framework. The estimator considers many potential priors and uses a simple statistic to choose the optimal solution. Our method outperforms its competitors for all finite data.

In this study we illustrate a Maximum Entropy (ME) methodology for modeling incomplete information and learning from repeated samples. The basis for this method has its roots in information theory and builds on the classical maximum entropy work of Janes (1957). We illustrate the use of this...

In this study we illustrate a Maximum Entropy (ME) methodology for modeling incomplete information and learning from repeated samples. The basis for this method has its roots in information theory and builds on the classical maximum entropy work of Janes (1957). We illustrate the use of this...