James O. Berger
The Arts & Sciences Professor of Statistics
James O. Berger is the Arts & Sciences Professor of Statistics at Duke University. He received his PhD from Cornell University in 1974. Before moving to Duke in 1997, he was in the Department of Statistics at Purdue University. From 2002–2010 he was the director of the Statistical and Applied Mathematical Sciences Institute (SAMSI). He has served as the president of the Institute of Mathematical Statistics (IMS) (1995–1996), chair of the Section on Bayesian Statistical Science of the American Statistical Association (ASA) (1995), president of the International Society for Bayesian Analysis (2004), and co-editor of the Annals of Statistics (1998–2000).
Professor Berger is a fellow of the ASA and the IMS. He has been awarded Guggenheim and Sloan Fellowships; the Committee of Presidents of Statistical Societies (COPSS) Presidents’ Award (1985); and the Sigma Xi Research Award (1993) at Purdue University for contribution of the year to science. He was the COPSS Fisher Lecturer in 2001 and the IMS Wald Lecturer in 2007. He was elected as a foreign member of the Spanish Real Academia de Ciencias in 2002, elected to the USA National Academy of Sciences in 2003, awarded an honorary Doctor of Science degree from Purdue University in 2004, and was named an Honorary Professor at East China Normal University in 2011.
(Nov 14, 2013)
Reproducibility of Science: P-values and Multiplicity
Published scientific findings seem to be increasingly failing efforts at replication. This is undoubtedly due to many sources, including specifics of individual scientific cultures and overall scientific biases such as publication bias. While these will be briefly discussed, the talk will focus on the all-too-common misuse of p-values and failure to properly account for multiplicities as two likely major contributors to the lack of reproducibility. The Bayesian approaches to both testing and multiplicity will be highlighted as possible general solutions to the problem.
(Nov 15, 2013)
Risk Assessment for Pyroclastic Flows
The problem of risk assessment for rare natural hazards – such as volcanic pyroclastic flows – is addressed, and illustrated with the Soufriere Hills Volcano on the island of Montserrat. Assessment is approached through a combination of mathematical computer modeling, statistical modeling of geophysical data, and extreme-event probability computation.A mathematical computer model of the natural hazard is used to provide the needed extrapolation to unseen parts of the hazard space. Statistical modeling of the available geophysical data is needed to determine the initializing distribution for exercising the computer model. In dealing with rare events, direct simulations involving the computer model are prohibitively expensive, so computation of the risk probabilities requires a combination of adaptive design of computer model approximations (emulators) and rare event simulation.