Robert L Wolpert, Professor of Statistical Science and the Environment

Robert L Wolpert
Office Location:  211C Old Chemistry Bldg.
Office Phone:  1-919-684-3275
Email Address: send me a message
Web Page:  http://www.isds.duke.edu/~rlw/

Teaching (Fall 2012):

Education:

PhD (Mathematics)Princeton University1976
ABCornell University1972
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

Current Ph.D. Students  

  • Natesh Pillai  
  • Chong Tu  
  • Jingqin '. Luo  
  • Gangqiang Xia  
  • Casey Lichtendahl  
  • Dawn Banard  
  • Zhenglei Gao  
  • Leanna House  
  • Joe Lucas  
  • Floyd Bullard  
Representative Publications

  1. Francesca Dominici and Giovanni Parmigiani and Kenneth Reckhow and Robert L. Wolpert, Combining Information from Related Regressions, Journal of Agricultural, Biological and Environmental Statistics, vol. 2 no. 3 (September, 1997), pp. 313--332
  2. 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
  3. Robert L. Wolpert and Murad Taqqu, Fractional Ornstein-Uhlenbeck L\'evy Processes and the Telecom Process: Upstairs and Downstairs, Signal Processing, vol. 85 no. 8 (2005), pp. 1523-1545
  4. Francesca Dominici and Giovanni Parmigiani and Robert L. Wolpert and Vic Hasselblad, 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--29
  5. Robert L. Wolpert and Kerrie L. 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 (August, 2004)
  6. Robert L. Wolpert and Katja Ickstadt, Reflecting Uncertainty in Inverse Problems: A Bayesian Solution using L\'evy Processes, Inverse Problems, vol. 20 no. 6 (2004), pp. 1759--1771
  7. R.L. 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
  8. 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
  9. Michael L. Lavine and Larry Wasserman and Robert L. Wolpert, Bayesian inference with specified prior marginals, Journal of the American Statistical Association, vol. 86 no. 416 (December, 1991), pp. 964--971
  10. 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
  11. Robert L. Wolpert and Katja Ickstadt, Poisson/gamma random field models for spatial statistics, Biometrika, vol. 85 no. 2 (June, 1998), pp. 251--267
  12. Nicola G. Best and Katja Ickstadt and Robert L. Wolpert, 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
  13. James O. Berger and Lawrence D. Brown and Robert L. 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
  14. James O. Berger and Brunero Liseo and Robert L. Wolpert, Integrated likelihood methods for eliminating nuisance parameters, Statistical Science, vol. 14 no. 1 (February, 1999), pp. 1--28