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### Robert L. Wolpert, Professor of Statistical Science and Professor in the Division of Environmental Sciences and Policy

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 <STRONG>Likelihood

Principle</STRONG>, 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. <P> My current (2015-2016)

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.

Office Location: | 211C Old Chemistry, Durham, NC 27708-0251 |

Office Phone: | (919) 684-3275 |

Email Address: | |

Web Page: | http://www.stat.duke.edu/~rlw/ |

**Teaching (Fall 2016):**

- STA 711.01,
*PROBABIL/MEASURE THEORY*Synopsis- Soc/Psych 126, MW 01:25 PM-02:40 PM

**Office Hours:**- Vary from term to term. Check course website.

**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)- F Dominici, G Parmigiani, KH Reckhow and RL Wolpert,
*Combining Information from Related Regressions*, Journal of Agricultural, Biological, and Environmental Statistics, vol. 2 no. 3 (1997), pp. 313-332, ISSN 1085-7117 [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 - RL Wolpert and MS Taqqu,
*Fractional Ornstein-Uhlenbeck Lévy processes and the Telecom process: Upstairs and downstairs*, Signal Processing, vol. 85 no. 8 (2005), pp. 1523-1545 [doi] [abs] - F Dominici, G Parmigiani, RL Wolpert and V Hasselblad,
*Meta-Analysis of Migraine Headache Treatments: Combining Information From Heterogeneous Designs*, Journal of the American Statistical Association, vol. 94 no. 445 (1999), pp. 16-28, ISSN 0162-1459 [abs] - RL Wolpert and KL Mengersen,
*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 (2004), pp. 450-471 [doi] [abs] - RL Wolpert and K Ickstadt,
*Reflecting uncertainty in inverse problems: A Bayesian solution using Lévy processes*, Inverse Problems, vol. 20 no. 6 (2004), pp. 1759-1771, ISSN 0266-5611 [doi] [abs] - RL Wolpert,
*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 - M LAVINE, L WASSERMAN and R WOLPERT,
*BAYESIAN-INFERENCE WITH SPECIFIED PRIOR MARGINALS*, JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION, vol. 86 no. 416 (December, 1991), pp. 964-971, ISSN 0162-1459 [Gateway.cgi], [doi] - 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 - RL Wolpert and K Ickstadt,
*Poisson/gamma random field models for spatial statistics*, Biometrika, vol. 85 no. 2 (1998), pp. 251-267, ISSN 0006-3444 [abs] - NG Best, K Ickstadt and RL Wolpert,
*Spatial Poisson Regression for Health and Exposure Data Measured at Disparate Resolutions*, Journal of the American Statistical Association, vol. 95 no. 452 (2000), pp. 1076-1088, ISSN 0162-1459 [abs] - J BERGER, L BROWN and R WOLPERT,
*A UNIFIED CONDITIONAL FREQUENTIST AND BAYESIAN TEST FOR FIXED AND SEQUENTIAL SIMPLE HYPOTHESIS-TESTING*, ANNALS OF STATISTICS, vol. 22 no. 4 (December, 1994), pp. 1787-1807, ISSN 0090-5364 [Gateway.cgi], [doi] - JO Berger, B Liseo and RL Wolpert,
*Integrated Likelihood Methods for Eliminating Nuisance Parameters*, Statistical Science, vol. 14 no. 1 (1999), pp. 1-28 [abs]

- F Dominici, G Parmigiani, KH Reckhow and RL Wolpert,