Adrian Raftery

Ph.D. 1980, University of Paris VI. Social mobility and family structure, educational transitions, demographic transition, environmental statistics, Bayesian statistics.

Department: Statistics & Sociology
Position: Blumstein-Jordan Professor
Email: click here
Phone: (206) 543-4505, (206) 221-6873
Box: 354320
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Recent Publications
Current Research Projects

Research Summary:

Adrian E. Raftery is the Blumstein-Jordan professor of Statistics and Sociology and directorof the Center for Statistics and the Social Sciences. In November 2006 he was awarded the 2006 Jerome Sacks Award for Cross-Disciplinary Research by the National Institute of Statistical Sciences "for outstanding contributions at the interface of the statistical sciences and the social, environmental and health sciences, as well as methodological research on Bayesian model selection and averaging."

His research focuses on statistical methodology for the social sciences and other applications, in four main current areas: inference for deterministic simulation models, model-based clustering, Bayesian model selection, and social networks.

Deterministic simulation models arise in many contexts. One of the first and most prominent examples is the population projection models that underlie most formal demography and population forecasting. Statisticians have tended to avoid such models, but they dominate many areas of science, social science and engineering, and accounting for the uncertainties involved is important, but not often done with much precision or rigor. Raftery developed new methods for doing this in the context of population dynamics models, of whales rather than of humans, in work for the International Whaling Commission. He continued this work in the area of environmental risk assessment models for the EPA. He is currently working with Sam Clark to extend such methods to population projection models and linked demographic-disease models. He is also working on similar methods for probabilistic weather forecasting.

Model-based clustering is the effort to develop cluster analysis by putting it on a solid and principled statistical footing, in contrast to the somewhat ad hoc methods that have dominated cluster analysis until recently. A great deal of progress has been made on this in the past 10 years, and it has had an impact on several disciplines, including,most recently, the analysis of gene expression data. Raftery has given major invited talks at chemistry, astronomy,
psychology, genomics and statistics conferences on this topic.

Model selection in statistics is at the core of hypothesis testing and causal inference. Raftery has been involved over the past 20 years in developing Bayesian model selection and Bayesian model averaging as a methodology for doing this, to replace the reliance on p values and the related significance tests, that have many shortcomings. This has had a substantial impact in several disciplines, including sociology and demography. His current work on this issue focuses on developing appropriate prior distributions for model selection, and methods for choosing the number of groups and the appropriate model for cluster analysis.

Social network analysis has many important substantive applications,including projecting the spread of epidemics such as AIDS, answering questions about phone caller networks, and assessing the dynamics of terrorist networks. The most general way to answer a wide range of questions about such data is to build a statistical model, and recent research has focused on this, notably the so-called p* models. These have awkward features, however, and with Peter Hoff of CSSS and Mark Handcock, Raftery has been investigating an alternative based on the concept of a latent social space, that can be estimated using modern Markov chain Monte Carlo Bayesian methods. This approach seems to work well in results to date.

Dr. Raftery was identified as the most cited mathematician in the world for the period 1995-2005, according to the Institute for Scientific Information.

Recent Publications:

Raftery, A. E.; Alkema, L., (Forthcoming), Discussions of "Estimates of human immunodeficiency virus prevalence and proportion diagnosed based on Bayesian multiparameter synthesis of surveillance data", Journal of the Royal Statistical Society.

Annest, A.; Bumgarner, R. E.; Raftery, A. E.; Yeung, K. Y., (2009), Iterative Bayesian Model Averaging: a method for the application of survival analysis to high-dimensional microarray data, BMC bioinformatics, 10.

Alkema, L.; Raftery, A. E.; Brown, T., (2008), Bayesian melding for estimating uncertainty in national HIV prevalence estimates, SEXUALLY TRANSMITTED INFECTIONS, 84, i11-i16.

Brown, T.; Salomon, J. A.; Alkema, L.; Raftery, A. E.; Gouws, E., (2008), Progress and challenges in modelling country-level HIV/AIDS epidemics: the UNAIDS Estimation and Projection Package 2007, SEXUALLY TRANSMITTED INFECTIONS, 84, i5-i10.

Chu, V. T.; Gottardo, R.; Raftery, A. E.; Bumgarner, R. E.; Yeung, K. Y., (2008), MeV+R: using MeV as a graphical user interface for Bioconductor applications in microarray analysis, Genome biology, 9: 7.

Gottardo, R.; Raftery, A. E., (2008), Markov Chain Monte Carlo With Mixtures of Mutually Singular Distributions, Journal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America., 17: 4, 949.

Alkema, L.; Raftery, A. E.; Clark, S. J., (2007), Probabilistic Projections of HIV Prevalence Using Bayesian Melding, The Annals of Applied Statistics, 1: 1, 229-248.

Berrocal, V. J.; Raftery, A. E.; Gneiting, T., (2007), Combining Spatial Statistical and Ensemble Information in Probabilistic Weather Forecasts, Monthly Weather Review, 135: 4, 1386-1402.

Fraley, C.; Raferty, A. E., (2007), Model-based methods of classification: Using the mclust software in chemometrics, Journal of Statistical Software, 18.

Fraley, C.; Raftery, A. E., (2007), Bayesian Regularization for Normal Mixture Estimation and Model-Based Clustering, Journal of classification., 24: 2, 155.

Gneiting, T.; Raftery, A. E., (2007), Strictly Proper Scoring Rules, Prediction, and Estimation, Journal of the American Statistical Association., 102: 477, 359.

Gneiting, T.; Raftery, A. E.; Balabdaoui, F., (2007), Probabilistic forecasts, calibration and sharpness, Journal of the Royal Statistical Society: Series B, 69: 2, 243-268.

Hamill, T. M.; Wilson, L. J.; Beauregard, S.; Raftery, A. E.; Verret, R., (2007), Comments on "Calibrated Surface Temperature Forecasts from the Canadian Ensemble Prediction System Using Bayesian Model Averaging" Reply, Monthly Weather Review, 135: 12, 4226-4237.

Handcock, M. S.; Raferty, A. E.; Tantrum, J., (2007), Model-Based Clustering for Social Network, Journal of the Royal Statistical Society: Series A, 170: 2, 301-354.

Oh, M.-S.; Raftery, A. E., (2007), Model-Based Clustering With Dissimilarities: A Bayesian Approach, Journal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America., 16: 3, 559.

Raferty, A. E.; Newton, M. A.; Satagopan, J. M.; Krivitsky, P., (2007), Estimating the Integrated Likeliehood via Posterior Simulation Using the Harmonic Mean Identity (with Discussion), Bayesian statistics 8 : proceedings of the eighth Valencia International Meeting, June 2-6, 2006, Bernardo, J. M., 1-45, Oxford University Press, Oxford ; New York.

Sevcikova, H.; Raferty, A. E.; Waddell, P., (2007), Assessing Uncertainty in Urban Simulations Using Bayesian Melding, Transportation Research B, 41, 652-69.

Sloughter, J. M.; Raftery, A. E.; Gneiting, T.; Fraley, C., (2007), Probabilistic Quantitative Precipitation Forecasting Using Bayesian Model Averaging, Monthly Weather Review, 135: 9, 3209-3220.

Wilson, L. J.; Beauregard, S.; Raftery, A. E.; Verret, R., (2007), Calibrated Surface Temperature Forecasts from the Canadian Ensemble Prediction System Using Bayesian Model Averaging, Monthly Weather Review, 135: 4, 1364-1385.

Czado, C. C.; Raferty, A. E., (2006), Choosing the Link Function and Accounting for Link Uncertainty in Generalized Linear Models using Bayes Factors, Statistical Papers, 47, 419-42.

Forbes, F.; Raftery, A. E.; Peyrard, N.; Fraley, C.; Georgian-Smith, D.; Goldhaber, D. M., (2006), Model-Based Region-of-Interest Selection in Dynamic Breast MRI, Journal of Computer Assisted Tomography, 30: 675-87.

Fraley, C.; Raferty, A. E., (2006), Some applications of model-based clustering in chemistry, R News, 6: 3, 17-23.

Fraley, C.; Raferty, A. E., (2006), Model-based microarray image analysis, R News, 6: 5, 6-63.

Gottardo, R.; Raftery, A. E.; Yee Yeung, K.; Bumgarner, R. E., (2006), Bayesian Robust Inference for Differential Gene Expression in Microarrays with Multiple Samples, Biometrics, 62: 1, 10-18.

Gottardo, R.; Raftery, A. E.; Yeung, K. Y.; Bumgarner, R. E., (2006), Applications and Case Studies - Quality Control and Robust Estimation for cDNA Microarrays With Replicates, Journal of the American Statistical Association, 101: 473, 30.

Raferty, A. E.; Dean, N., (2006), Variable Selection for Model-Based Clustering, Journal of the American Statistical Association, 101, 168-178.

Raftery, A. E.; Dean, N., (2006), Theory and Methods - Variable Selection for Model-Based Clustering, Journal of the American Statistical Association, 101: 473, 168.

Steele, R. J.; Raftery, A. E.; Emond, M. J., (2006), Computing Normalizing Constants for Finite Mixture Models via Incremental Mixture Importance Sampling (IMIS), Journal of Computational and Graphical Statistics, 15: 3, 712.

Tewson, P.; Raftery, A. E., (2006), Real-Time Calibrated Probabilistic Forecasting Website, Bulletin of the American Meteorological Society, 7, 880-882.

Dean, N.; Raftery, A. E., (2005), Normal uniform mixture differential gene expression detection for cDNA microarrays, BMC Bioinformatics, 6, 173.

Fraley, C.; Raftery, A. E.; Wehrens, R., (2005), Incremental Model-Based Clustering for Large Datasets with Small Clusters, Journal of Computational and Graphical Statistics, 14: 529-546.

Fuentes, M.; Raftery, A. E., (2005), Model evaluation and spatial interpolation by Bayesian combination of observations with outputs from numerical models, Biometrics, 66, 36–45.

Gneiting, T.; Raftery, A. E., (2005), Weather forecasting with ensemble methods, Science, 310, 248-249.

Gneiting, T.; Raftery, A. E.; Westveld, A. H.; Goldman, T., (2005), Calibrated Probabilistic Forecasting Using Ensemble Model Output Statistics and Minimum CRPS Estimation, Monthly Weather Review, 133: 5, 1098-1118.

Li, Q.; Fraley, C.; Bumgarner, R. E.; Yeung, K. Y.; Raftery, A. E., (2005), Donuts, Scratches and Blanks: Robust Model-Based Segmentation of Microarray Images, Bioinformatics, 21: 12, 2875-2882.

Murtagh, F.; Raftery, A. E.; Starck, J. L., (2005), Bayesian inference for multiband image segmentation via model-based cluster trees, Image and Vision Computing, 23, 587-596.

Raftery, A. E.; Gneiting, T.; Balabdaoui, F.; Polakowski, M., (2005), Using Bayesian Model Averaging to Calibrate Forecast Ensembles., Monthly Weather Review, 133, 1155-1174.

Raftery, A. E.; Painter, I.; Volinsky, C. T., (2005), BMA: An R package for Bayesian Model Averaging, R News, 5: 2, 2-8.

Walsh, D. C. I.; Raftery, A. E., (2005), Classification of mixtures of spatial point processes via partial Bayes factors, Journal of Computational and Graphical Statistics, 14, 139-154.

Yeung, K. Y.; Bumgarner, R. E.; Raftery, A. E., (2005), Bayesian Model Averaging: Development of an improved multi-class, gene selection and classification tool for microarray data, Bioinformatics, 21: 10, 2394-2402.

Current Research Projects PI: