Professor
Roger J.-B. Wets
Department of Mathematics
University of California
Davis, California, USA
Statistics and optimization have been closely linked from the very outset. The search for a `best' estimator (least squares, maximum likelihood, etc.) certainly relies on optimization tools. However, it's only relatively recently, more specifically in connection with the development of an approximation and sampling theory for stochastic programming problems, that the potential contributions that variational analysis can make to the field of statistical estimation have come to light. In fact, it allows us to build a new paradigm for the framing of statistical estimation questions that justifies and opens the door to the full use of optimization techniques in statistical estimation.