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Robert L. Wolpert, Professor Emeritus of Statistical Science
Please note: Robert has left the "Probability: Theory and Applications" group at Duke University; some info here might not be up to date. I'm a stochastic modeler-- I build computer-resident mathematical models for complex systems, and invent and program numerical algorithms for making inference from the models. Usually this involves predicting things that haven't been measured (yet). Always it involves managing uncertainty and making good decisions when some of the information we'd need to be fully comfortable in our decision-making is unknown.
Originally trained as a mathematician specializing in probability theory and stochastic processes, I was drawn to statistics by the interplay between theoretical and applied research- with new applications suggesting what statistical areas need theoretical development, and advances in theory and methodology suggesting what applications were becoming practical and so interesting. Through all of my statistical interests (theoretical, applied, and methodological) runs the unifying theme of the Likelihood Principle, a constant aid in the search for sensible methods of inference in complex statistical problems where commonly-used methods seem unsuitable. Three specific examples of such areas are:
- Computer modeling, the construction and analysis of fast small Bayesian
statistical emulators for big slow simulation models;
- Meta-analysis, of how we can synthesize evidence of different sorts about
a statistical problem; and
- Nonparametric Bayesian analysis, for applications in which common
parametric families of distributions seem unsuitable.
Many of the methods in common use in each of these areas are hard or impossible to justify, and can lead to very odd inferences that seem to misrepresent the statistical evidence. Many of the newer approaches abandon the ``iid'' paradigm in order to reflect patterns of regional variation, and abandon familiar (e.g. Gaussian) distributions in order to reflect the heavier tails observed in realistic data, and nearly all of them depend on recent advances in the power of computer hardware and algorithms, leading to three other areas of interest:
- Spatial Statistics,
- Statistical Extremes, and
- Statistical computation.
I have a special interest in developing statistical methods for application to problems in Environmental Science, where traditional methods often fail. Recent examples include developing new and better ways to estimate the mortality to birds and bats from encounters with wind turbines; the development of nonexchangeable hierarchical Bayesian models for synthesizing evidence about the health effects of environmental pollutants; and the use of high-dimensional Bayesian models to reflect uncertainty in mechanistic environmental simulation models. My current research involves modelling and Bayesian inference of dependent time series and (continuous-time) stochastic processes with jumps (examples include work loads on networks of digital devices; peak heights in mass spectrometry experiments; or multiple pollutant levels at spatially and temporally distributed sites), problems arising in astrophysics (Gamma ray bursts) and high-energy physics (heavy ion collisions), and the statistical modelling of risk from, e.g., volcanic eruption.
- Contact Info:
- Office Hours:
- By appointment. Send an e-mail to find a convenient time for both of us.
- Education:
Ph.D. | Princeton University | 1976 |
B.A. | Cornell University | 1972 |
AB | Cornell University | 1972 |
- Specialties:
-
Statistical Modeling
statistics Bayesian Statistics ecology Stochastic Processes environmental toxicology Spatial Statistics
- Research Interests: Nonparametric Bayesian Models, Stochastic Processes & Time Series, and Spatial Statistics
- Areas of Interest:
- Spatial statistics
Stochastic Processes, Stochastic Analysis Non-parametric Bayesian analysis Modeling & Decision Support in Complex Systems Environmental & Epidemiological Applications
- Keywords:
- Bayes Theorem • Computer Simulation • Crisis Intervention • Decision Support Techniques • Environmental Monitoring • Meta-Analysis as Topic • Models, Biological • Models, Statistical • Models, Theoretical • Ozone • Probability • Rats, Inbred Strains • Rheology • Risk • Rivers • Taste • Toxicology • Transportation • Water Microbiology
- Current Ph.D. Students
(Former Students)
- Natesh Pillai
- Chong Tu
- Jingqin '. Luo
- Gangqiang Xia
- Casey Lichtendahl
- Dawn Banard
- Zhenglei Gao
- Leanna House
- Joe Lucas
- Floyd Bullard
- Representative Publications
(More Publications)
- Dominici, F; Parmigiani, G; Reckhow, K; Wolpert, RL, Combining Information from Related Regressions,
Journal of Agricultural, Biological and Environmental Statistics, vol. 2 no. 3
(September, 1997),
pp. 313-332, Springer Nature, ISSN 1085-7117 [doi] [abs]
- James O. Berger and Robert L. Wolpert, The Likelihood Principle: A Review, Generalizations, and Statistical Implications (with discussion), IMS Lecture Notes-Monograph Series, vol. 6
(1988), Institute of Mathematical Statistics, Hayward, CA
- Wolpert, RL; Taqqu, MS, Fractional Ornstein-Uhlenbeck Lévy processes and the Telecom process: Upstairs and downstairs,
Signal Processing, vol. 85 no. 8
(August, 2005),
pp. 1523-1545, Elsevier BV [doi] [abs]
- Dominici, F; Parmigiani, G; Wolpert, RL; Hasselblad, V, Meta-Analysis of Migraine Headache Treatments: Combining Information from Heterogeneous Designs,
Journal of the American Statistical Association, vol. 94 no. 445
(March, 1999),
pp. 16-28, ISSN 0162-1459 [doi] [abs]
- Wolpert, RL; Mengersen, KL, Adjusted likelihoods for synthesizing empirical evidence from studies that differ in quality and design: Effects of environmental tobacco smoke,
Statistical Science, vol. 19 no. 3
(August, 2004),
pp. 450-471, Institute of Mathematical Statistics [doi] [abs]
- Wolpert, RL; Ickstadt, K, Reflecting uncertainty in inverse problems: A Bayesian solution using Lévy processes,
Inverse Problems, vol. 20 no. 6
(December, 2004),
pp. 1759-1771, IOP Publishing, ISSN 0266-5611 [doi] [abs]
- Wolpert, RL, Invited discussion of `On the Probability of Observing Misleading Statistical Evidence', by R. Royall,
J. American Statistical Assoc., vol. 95 no. 451
(2000),
pp. 771-772
- N.G. Best, K. Ickstadt & R.L. Wolpert, Spatial Poisson regression for health and exposure data measured at disparate spatial scales,
J. American Statistical Assoc., vol. 95 no. 452
(2000),
pp. 1076-1088
- Lavine, M; Wasserman, L; Wolpert, RL, Bayesian inference with specified prior marginals,
Journal of the American Statistical Association, vol. 86 no. 416
(January, 1991),
pp. 964-971, JSTOR, ISSN 0162-1459 [Gateway.cgi], [doi] [abs]
- Robert L. Wolpert and Katja Ickstadt, Simulation of L\'evy Random Fields,
in Practical Nonparametric and Semiparametric Bayesian Statistics, Lecture Notes in Statistics, edited by Dipak K. Dey and Peter M\^^buller and Debajyoti Sinha, vol. 133
(1998),
pp. 227--242, Springer-Verlag, New York, NY, ISBN 0-387-98517-4
- Wolpert, RL; Ickstadt, K, Poisson/gamma random field models for spatial statistics,
Biometrika, vol. 85 no. 2
(January, 1998),
pp. 251-267, Oxford University Press (OUP), ISSN 0006-3444 [doi] [abs]
- Best, NG; Ickstadt, K; Wolpert, RL, Spatial Poisson Regression for Health and Exposure Data Measured at Disparate Resolutions,
Journal of the American Statistical Association, vol. 95 no. 452
(December, 2000),
pp. 1076-1088, Informa UK Limited, ISSN 0162-1459 [doi] [abs]
- Berger, JO; Brown, LD; Wolpert, RL, A Unified Conditional Frequentist and Bayesian Test for Fixed and Sequential Simple Hypothesis Testing,
The Annals of Statistics, vol. 22 no. 4
(December, 1994),
pp. 1787-1807, Institute of Mathematical Statistics, ISSN 0090-5364 [Gateway.cgi], [doi]
- Berger, JO; Liseo, B; Wolpert, RL, Integrated likelihood methods for eliminating nuisance parameters,
Statistical Science, vol. 14 no. 1
(January, 1999),
pp. 1-22, Institute of Mathematical Statistics [doi] [abs]
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