Information-Theoretic Estimation and Data Analysis

In this talk I will review the state of Information Theoretic and Entropy Methods used for recovering the information from (small, large and/or complex) data. I will discuss the connecting theme among these methods and will provide a more detailed discussion of the sub-class of methods that treat the observed sample moments as stochastic. The resulting method uses minimal distributional assumptions, performs well (relative to current methods of estimation) and uses efficiently all the available information (hard and soft data). This method is computationally efficient. I will present the basic ideas using a number of empirical examples taken from economics, physics, image reconstruction and operation research. Studying these examples will provide a way for a synthesis of that class of models and connecting it to the more traditional methods of data analysis. I will conclude with some thoughts on potential future developments.

Dr. Amos Golan
Department of Economics
American University