Adrian E. Raftery

Adrian is Professor of Statistics and Sociology and a faculty affiliate of the Center for Statistics and the Social Sciences and the Center for Studies in Demography and Ecology at the University of Washington. He works on the development of new statistical methods for the social, environmental and health sciences.

• Working Group on Applied, Bayesian and Computational Statistics, Spring 2013

• Summer Working Group on Model-Based Clustering, Bologna, July 2013

News

• Adrian was elected to the Royal Irish Academy.

• Adrian was awarded the Parzen Prize for Statistical Innovation. Here is a news release.

• Bailey Fosdick was a Poster Session Award Winner at the Population Association of America 2013 Annual Meeting.

• An interview with Adrian was published in Proceedings of the National Academy of Sciences.

Recent Publications (since 2012)

Population Projection

• Bayesian probabilistic population projections for all countries.
• Bayesian Probabilistic Projections of Life Expectancy for All Countries.
• Bayesian Population Projections for the United Nations.
• Regional Probabilistic Fertility Forecasting by Modeling Between-Country Correlations.

Population Estimation

• Estimating Demographic Parameters with Uncertainty from Fragmentary Data.
• Estimating the Total Fertility Rate from Multiple Imperfect Data Sources and Assessing its Uncertainty.
• Bayesian Reconstruction of Past Populations and Vital Rates by Age for Developing and Developed Countries.
• Modeling HIV/AIDS epidemics: revised approach in the UNAIDS Estimation and Projection Package 2011.

• Dynamic Logistic Regression and Dynamic Model Averaging for Binary Classification.
• Probabilistic Wind Vector Forecasting using Ensembles and Bayesian Model Averaging.

Gene expression data

• Integrating External Biological Knowledge in the Construction of Regulatory Networks from Time-series Expression Data.
• Predicting relapse prior to transplantation in chronic myeloid leukemia by integrating expert knowledge and expression data.

Other statistical methodology

• Fast Inference for the Latent Space Network Model Using a Case-Control Approximate Likelihood.
• Estimating the Correlation in Bivariate Normal Data with Known Variances and Small Sample Sizes.
• mclust Version 4 for R: Normal Mixture Modeling for Model-Based Clustering, Classification, and Density Estimation.

These papers are being made available here to facilitate the timely dissemination of scholarly work; copyright and all related rights are retained by the copyright holders.

Updated May 3, 2013; last major update May 2, 2013

Copyright 2005-2013 by Adrian E. Raftery; all rights reserved.