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Adrian Raftery: Bayesian Estimation and MCMC Research

My research on Bayesian estimation has focused on the use of Bayesian hierarchical models for a range of applications; see below. For most of the applications from 1995 onwards I used MCMC; before that I had to resort to a range of "clever" tricks, which fortunately now are less necessary thanks to MCMC. (This page just has Bayesian estimation papers, not papers on Bayesian model averaging, model selection and hypothesis testing.)

I have also been interested in methods for the implementation of MCMC, including questions such as: How many iterations are needed? How many iterations should we skip for approximate independence? How can we set the variance parameters (potentially different ones for each simulated model parameter) in random walk Metropolis-Hastings? Steven Lewis and I proposed a three-simulation methodology for this (Raftery and Lewis 1992, 1996). The gibbsit software for doing this is freely available in Splus and Fortran, and is also part of the CODA convergence diagnostics and output analysis R package for MCMC.

Papers

Maire, F., Friel, N., Mira, A. and Raftery, A.E. (2016). Adaptive Incremental Mixture Markov chain Monte Carlo. Technical Report no. 643, Department of Statistics, University of Washington. Also arXiv:1604.08016.

Azose, J. J., Ševčíková , H. and Raftery, A.E. (2016). Probabilistic population projections with migration uncertainty. Proceedings of the National Academy of Sciences 113:6460-6465.

Friel, N., Wyse, J. and Raftery, A.E. (2016). Interlocking directorates in Irish companies using bipartite networks: a latent space approach. Proceedings of the National Academy of Sciences, 113: 6629-6634.

Wheldon, M.C., Raftery, A.E., Clark, S.J. and Gerland, P. (2016). Bayesian population reconstruction of female populations for less developed and developed countries. Population Studies 70:21-37.

Wheldon, M.C., Raftery, A.E., Clark, S.J. and Gerland, P. (2015). Bayesian Reconstruction of Two-Sex Populations by Age: Estimating Sex Ratios at Birth and Sex Ratios of Mortality. Journal of the Royal Statistical Society, Series A: Statistics in Society 178:977-1007.

Maltiel, R., Raftery, A.E., McCormick, T.H. and Baraff, A. (2015). Estimating Population Size Using the Network Scale Up Method. Annals of Applied Statistics, 9:1247-1277.

Azose, J.J. and Raftery, A.E. (2015). Bayesian Probabilistic Projection of International Migration Rates. Demography 52:1627-1650.

Bao, L, Raftery, A.E. and Reddy, A. (2015). Estimating the Sizes of Populations at Risk of HIV Infection in Bangladesh Using a Bayesian Hierarchical Model. Statistics and Its Interface, 8:125-136.

Raftery, A.E., Alkema, L. and Gerland, P. (2014). Bayesian Population Projections for the United Nations. Statistical Science, 29:58-68.

Raftery, A.E., Chunn, J.L., Gerland, P. and Ševčíková , H. (2013). Bayesian Probabilistic Projections of Life Expectancy for All Countries. Demography, 50:777-801.

Wheldon, M., Raftery, A.E., Clark, S.J. and Gerland, P. (2013). Estimating Demographic Parameters with Uncertainty from Fragmentary Data. Journal of the American Statistical Association, 108:96-110.

Raftery, A.E., Li. N., Ševčíková , H., Gerland, P. and Heilig, G.K. (2012). Bayesian probabilistic population projections for all countries. Proceedings of the National Academy of Sciences 109:13915-13921.

Raftery, A.E., Niu, X., Hoff, P.D. and Yeung, K.Y. (2012). Fast Inference for the Latent Space Network Model Using a Case-Control Approximate Likelihood. Journal of Computational and Graphical Statistics, 21:909-919.

Fosdick, B.K. and Raftery, A.E. (2012). Estimating the Correlation in Bivariate Normal Data with Known Variances and Small Sample Sizes. The American Statistician, 66:34-41.

Alkema, L., Raftery, A.E., Gerland, P., Clark, S.J. and Pelletier, F. (2012). Estimating the Total Fertility Rate from Multiple Imperfect Data Sources and Assessing its Uncertainty. Demographic Research 26:331-362.

Alkema, L., Raftery, A.E., Gerland, P., Clark, S.J., Pelletier, F., Buettner, T. and Helig, G. (2011). Probabilistic Projections of the Total Fertility Rate for All Countries. Demography 48:815-839.

Raftery, A.E. and L. Bao. (2010). Estimating and Projecting Trends in HIV/AIDS Generalized Epidemics Using Incremental Mixture Importance Sampling. Biometrics 66:1162-1173.

Gottardo, R. and Raftery, A.E. (2009). Markov chain Monte Carlo with mixtures of singular distributions. Journal of Computational and Graphical Statistics 17:949-975.

Krivitsky, P., Handcock, M.S., Raftery, A.E. and Hoff, P. (2009). Representing Degree Distributions, Clustering, and Homophily in Social Networks With Latent Cluster Random Ects Models. Social Networks 31:204-213.

Oh, M.-S. and Raftery, A.E. (2007). Model-based Clustering with Dissimilarities: A Bayesian Approach. Journal of Computational and Graphical Statistics, 16, 559-585.

Fraley, C. and Raftery, A.E. (2007). Bayesian Regularization for Normal Mixture Estimation and Model-Based Clustering. Journal of Classification, 24, 155-181.

Gottardo, R., Raftery, A.E., Yeung, K.Y. and Bumgarner, R.E. (2006). Robust Estimation of cDNA Microarray Intensities with Replicates. Journal of the American Statistical Association, 101, 30-40.

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

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

Byers, S.D. and Raftery, A.E. (2002). Bayesian Estimation and Segmentation of Spatial Point Processes using Voronoi Tilings. In Spatial Cluster Modelling (A.G. Lawson and D. G.T. Denison, eds.), London: Chapman and Hall/CRC Press. Earlier technical report version. (Postscript).

Hoff, P., Raftery, A.E. and Handcock, M.S. (2002). Latent Space Approaches to Social Network Analysis. Journal of the American Statistical Association, 97, 1090-1098.

Walsh, D.C.I and Raftery, A.E. (2002). Detecting mines in minefields with linear characteristics. Technometrics, 44, 34-44.

Oh, M.-S. and Raftery, A.E. (2001). Bayesian Multidimensional Scaling and Choice of Dimension. Journal of the American Statistical Association, 96, 1031-1044.

Lewis, S.M. and Raftery, A.E. (1999). Comparing explanations of fertility decline using event history models and unobserved heterogeneity. Sociological Methods and Research, 28, 35-60.

Raftery, A.E. and Zeh, J.E. (1998). Estimating bowhead whale, Balaena mysticetus, population size and rate of increase from the 1993 census. Journal of the American Statistical Association, 93, 451-463.

Petrone, S. and Raftery, A.E. (1997). A note on the Dirichlet process prior in Bayesian nonparametric inference with partial exchangeability. Statistics and Probability Letters, 36, 69-83.

Bensmail, H., Celeux, G., Raftery, A.E. and Robert, C. (1997). Inference in model-based cluster analysis. Statistics and Computing, 7, 1-10.

Kahn, M.J. and Raftery, A.E. (1996). Discharge rates of Medicare stroke patients to skilled nursing facilities: Bayesian logistic regression with unobserved heterogeneity. Journal of the American Statistical Association, 91, 29-41.

Raftery, A.E., Lewis, S.M., Aghajanian, A. and Kahn, M.J. (1996). Event history analysis of World Fertility Survey data. Mathematical Population Studies, 6, 129-153. Earlier technical report version (ps).

Raftery, A.E. and Lewis, S.M. (1996). Implementing MCMC. In Markov Chain Monte Carlo in Practice(W.R. Gilks, D.J. Spiegelhalter and S. Richardson, eds.), London: Chapman and Hall, pp. 115-130. Earlier version (ps).

Raftery, A.E. (1996). Hypothesis testing and model selection. In Markov Chain Monte Carlo in Practice(W.R. Gilks, D.J. Spiegelhalter and S. Richardson, eds.), London: Chapman and Hall, pp. 163--188. Earlier version (ps).

Raftery, A.E., Lewis, S.M. and Aghajanian, A. (1995). Demand or ideation? Evidence from the Iranian marital fertility decline. Demography, 32, 159-182.

Taplin, R.H. and Raftery, A.E. (1994). Analysis of agricultural field trials in the presence of outliers and fertility jumps. Biometrics, 50, 764-781.

Newton, M.A. and Raftery, A.E. (1994). Approximate Bayesian inference by the weighted likelihood bootstrap (with Discussion). Journal of the Royal Statistical Society, series B, 56, 3-48.

Raftery, A.E. and Schweder, T. (1993). Inference about the ratio of two parameters, with application to whale censusing. The American Statistician, 47, 259-264.

Raftery, A.E. and Zeh, J.E. (1993). Estimation of Bowhead Whale, Balaena mysticetus, population size (with Discussion). In Bayesian Statistics in Science and Technology: Case Studies (C. Gatsonis et al., eds.), New York: Springer-Verlag, pp. 163-240.

Raftery, A.E. and Lewis, S.M. (1992). How many iterations in the Gibbs sampler? In Bayesian Statistics 4 (J.M. Bernardo et al., editors), Oxford University Press, pp. 763-773. Earlier version (ps).

Raftery, A.E., Zeh, J.E., Yang, Q. and Styer, P.E. (1990). Bayes empirical Bayes interval estimation of bowhead whale, Balaena mysticetus, population size based upon the 1986 combined visual and acoustic census off Point Barrow, Alaska. Report of the International Whaling Commission, 40, 393-409.

Raftery, A.E. and Thompson, E.A. (1990). What is the probability of a serious nuclear reactor accident? Journal of Statistical Computation and Simulation, 36, 31-34.

Zeh, J.E., Turet, P., Gentleman, R. and Raftery, A.E. (1988). Population size estimation for the bowhead whale, Balaena mysticetus, based on 1985 and 1986 visual and acoustic data. Report of the International Whaling Commission, 38, 349-364.

Raftery, A.E., Turet, P. and Zeh, J.E. (1988). A parametric empirical Bayes approach to interval estimation of bowhead whales, Balaena mysticetus, population size. Report of the International Whaling Commission, 38, 377-388.

Raftery, A.E. and Thompson, E.A. (1988). How many nuclear reactor accidents? Journal of Statistical Computation and Simulation, 29, 347-350.

Raftery, A.E. (1988). Inference and prediction for the binomial N parameter: A hierarchical Bayes approach. Biometrika, 75, 223-228.

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 July 1, 2016

Copyright 2005-2016 by Adrian E. Raftery; all rights reserved.