Thomas Augustin is Professor at the Department of Statistics, Ludwig-Maximilians University (LMU) in Munich, and head of the group “Foundations of Statistics and their applications”. His research aims at a statistical methodology where data quality can be taken into account critically, resulting in generalized modelling of complex, non-idealized data in social sciences and biometrics. He has mainly published on imprecise probabilities in statistical inference and decision making, and on error measurement modelling.
Scott Ferson is director of the Institute for Risk and Uncertainty at the University of Liverpool in the UK. For many years he was senior scientist at Applied Biomathematics in New York and taught risk analysis at Stony Brook University. Dr. Ferson has over a hundred publications, mostly in risk analysis and uncertainty propagation, and is a fellow of the Society for Risk Analysis. His recent research, funded mostly by NIH and NASA, focuses on reliable statistical tools when empirical information is very sparse, and distribution-free methods for risk analysis.
Ryan Martin is an Associate Professor in the Department of Statistics at North Carolina State University. He obtained his PhD in Statistics from Purdue University in 2009 and has since worked in a number of different research areas, including asymptotics, Bayes and empirical Bayes inference, especially for high-dimensional problems, and the foundations of statistics. In particular, he is co-author of the monograph Inferential Models that presents a general framework for valid statistical inference based on belief functions.
Talk Title: Belief functions and valid statistical inference
Abstract: Belief functions originated with Dempster’s work in statistics, but they are almost entirely absent in the statistics mainstream. In this talk, I will argue that the properties of non-additive belief and plausibility functions are very much in line with classical statistical reasoning (e.g., p-values, confidence, etc) and, furthermore, that non-additivity — and not the familiar additivity of probability measures — is necessary for valid statistical inference. Of course, not all belief functions would be satisfactory in a given problem, and I will present a general inferential model framework for the construction of valid belief functions for statistical inference.