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Tom Shively

Thomas Shively

Professor of Statistics, Department of Information, Risk, and Operations Management

  • PhD, The University of Chicago, 1986
  • MBA, The University of Chicago, 1984
  • BA, Middlebury College, 1981

Dr. Tom Shively is a Professor of Statistics and the Joe B. Cook Professor of Business in the McCombs School of Business at The University of Texas at Austin. He received a BA degree in Mathematics from Middlebury College, an MBA degree from the University of Chicago, and a PhD degree in Statistics from the University of Chicago. Professor Shively has been on the statistics faculty in the McCombs School since 1986 and was IROM Department chair from 2002-2007. He has also taught in the Department of Statistics at the University of Auckland in New Zealand. He is a four-time recipient of the Outstanding Professor Award in the Full-Time and Executive MBA Programs and also won the Joe D. Beasley Award for Teaching Excellence in the MBA Program three times.

Professor Shively's research focuses on the development of new statistical methods and their applications. His methodological research is primarily in the areas of nonparametric regression models, hierarchical Bayes models and model selection techniques. Professor Shively has also done extensive applied work in the fields of marketing and environmental science. His research has appeared in many journals including the Journal of the American Statistical Association, Journal of the Royal Statistics Society, Series B, Journal of Econometrics, Journal of Time Series Analysis, Applied Statistics, Review of Economics and Statistics, Journal of Marketing Research, Marketing Science, Journal of Risk and Insurance, Atmospheric Environment and Environmental Science and Technology.

He is a past president of the Austin chapter of the American Statistical Association and a past chair of the Business and Economics section of the American Statistical Association. He has refereed papers for journals in a variety of fields including statistics, econometrics, marketing, environmental science, operations research, decision support systems, information systems, management science, and neural networks.