The research problems in this project belong mainly to four areas: Stochastic Optimization, Information Theory in Bayesian Analysis, Statistical Analysis in Chaotic Models, and Problems in Merging Micro-data Files. The stochastic optimization project is expected to develop a mostly automated and possibly fast converging algorithm for finding the global minimum of a complicated function. The investigations in information theory in the Bayesian analysis area are devoted to the application of various information measures to the development of new theories for choosing the better of the two experiments. Investigations under Chaos and Statistics pertain to Bayesian statistical analysis of chaotic nonlinear times series. The development of statistical theory for file-merging methodology, which is used heavily in evaluating policy implications by federal agencies, is a continuation of a project supported by NSF in the past.
