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Credit: © 2018 Duke University
On June 25, 2018, The Institute of Statistical Mathematics (ISM) associated with the Research Organization of Information and Systems (ROIS) and the Japan Statistical Society (JSS) have jointly announced that the awardee of the Second Akaike Memorial Lecture Award is Professor Mike West, the Arts & Sciences of Statistics & Decision Sciences, Department of Statistical Science, Duke University in USA, who has made significant contributions to the statistical sciences including Bayesian computation. The award ceremony and memorial lecture will be held in the plenary session of the Japanese Joint Statistical Meeting on September 10, 2018. This award was inaugurated in 2016 under the joint sponsorship of ISM and JSS, the main objective of which is to celebrate the outstanding achievements of the late Dr. Hirotugu Akaike who proposed the Akaike Information Criterion (AIC) in the 1970s.
Reasons for Selection Professor Mike West is a great pioneer in the field of Bayesian statistics and receives the Second Akaike Memorial Lecture Award for his wide-ranging and outstanding research accomplishments in both theoretical and practical aspects of this field.
Professor West has made significant contributions to the statistical sciences, including dynamic modeling, space-time data analysis, sparse modeling, Bayesian computation, and non-parametric Bayesian analysis. The applied fields to which Prof. West has contributed include finance, macroeconomics, climatology, and biology. His paper on Dirichlet Process mixture models, published in 1995, offered an easy-to-implement and efficient algorithm for the model. Previously, even though its usefulness had been understood theoretically, this model had not been used in practice due to difficulties in its computation. Professor West's algorithm represented a major contribution to the practical realization of the non-parametric Bayesian analysis. The magnitude of this influence is illustrated by the fact that the paper has been cited more than 2000 times [1]. In addition, in 2001 and 2006, Professor West published important papers pertaining to cancer prognosis using gene expression data, conducted jointly with leading molecular geneticists. These papers identified patterns of cell signaling pathway deregulation by combining signature-based predictions and developed predictive models for identifying types of breast cancer [2,3]. These outcomes led to the occurrence of a mega-trend in omics biology: biomarker discovery through gene expression profiling.
For these reasons, the nominating committee is proud to select Professor West as the awardee of the Second Akaike Memorial Lecture Award.
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[1] Escobar, M.D., West, M.(1995). Bayesian density estimation and inference using mixtures. Journal of the American Statistical Association 90:577-588.
[2] Bild, A.H., Yao, G., Chang, J.T., Wang, Q., Potti, A., Chasse, D., Joshi, M., Harpole, D., Lancaster, J.M., Berchuck, A., J.A. Olson, J.R.M., Dressman, H.K., West, M., Nevins, J.R.(2006). Oncogenic pathway signatures in human cancers as a guide to targeted therapies. Nature 439:353-357.
[3] West, M., Blanchette, C., Dressman, H.K., Huang, E.S., Ishida, S., R. Spang, H.Z., Marks, J.R., Nevins, J.R. (2001). Predicting the clinical status of human breast cancer utilizing gene expression profiles. Proceedings of the National Academy of Sciences 98:11462-11467.
The Akaike Memorial Lecture
Lecturer: Professor Mike West (Duke University)
Title: Bayesian Forecasting of Multivariate Time Series: Model Scalability, Structure Uncertainty and Decisions
Discussants: Dr. Jouchi Nakajima (Bank for International Settlements)
Dr. Christopher D. Glynn (University of New Hampshire)
Date/Time: September 10, 2018 (a.m.)
Venue: Chuo University, Tokyo, Japan
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