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Biography

James Scott received a B.S. in mathematics from the University of Texas, a master's in mathematics from the University of Cambridge, and a Ph.D in statistics from Duke University. His research interests include statistical model selection, time series analysis, graphical models, and other topics in Bayesian statistics.

Publications

Nicholas G. Polson, James Scott, and Jesse Windle. 2014. The Bayesian Bridge. Journal of the Royal Statistical Society: Series B (Statistical Methodology) 76(4), 713-733.
Nicholas G. Polson, James Scott, and Jesse Windle. 2013. Bayesian Inference for Logistic Models Using Pólya–Gamma Latent Variables. Journal of the American Statistical Association 108(504), 1339-1349.
P. Hahn, Carlos Carvalho, and James Scott. 2012. A Sparse Factor Analytic Probit Model for Congressional Voting Patterns. Journal of the Royal Statistical Society: Series C (Applied Statistics) 61(4), 619-635.
James Scott. 2012. Benchmarking historical corporate performance. Computational Statistics & Data Analysis 56(6), 1795-1807.
Nicholas Polson and James Scott. 2012. Local shrinkage rules, Levy processes and regularized regression. Journal of the Royal Statistical Society Series B-Statistical Methodology 774, 287-311.
M. J. Heaton and James Scott. 2010. Bayesian Computation and the Linear Model, in Frontiers of Statistical Decision Making and Bayesian Analysis, Ming-Hui Chen, Dipak Dey, Peter Mueller, Dongchu Sun, and Keying Ye, eds. Springer.
Jose M. Quintana, Carlos Carvalho, and James Scott. 2010. Bayesian Forecasting, Futures Markets, and Risk Modelling, in Handbook of Applied Bayesian Analysis, Anthony O'Hagan and Mike West, eds. Oxford University Press.
Carlos Carvalho, N.G. Polson, and James Scott. 2010. The Horsehoe Estimator for Sparse Signals. Biometrika 97, 465-480.
Carlos Carvalho, N. G. Polson, and James Scott. 2009. Handling Sparsity via the Horseshoe. Journal of Machine Learning Research W&CP 5, 73-80.
James Scott. 2009. Nonparametric Bayesian Multiple Testing for Longitudinal Performance Stratification. The Annals of Applied Statistics 3(4), 1655-1674.
Carlos Carvalho and James Scott. 2009. Objective Bayesian Model Selection in Gaussian Graphical Models. Biometrika 96(3), 497-512.
James Scott and Carlos Carvalho. 2008. Feature-Inclusion Stochastic Search for Gaussian Graphical Models. Journal of Computational and Graphical Statistics 17(4), 790-808.
James Scott and J. O. Berger. 2006. An exploration of aspects of Bayesian multiple testing. Journal of Statistical Planning and Inference 136.7, 2144-62.
T. von Hippel, W. H. Jefferys, James Scott, N. Stein, D. E. Winget, S. DeGennaro, A. Dam, and E. Jeffery. 2006. Inverting color-magnitude diagrams to access precise star cluster parameters: a Bayesian approach. The Astrophysical Journal 645.2, 1436-47.

Professional Awards

Savage Award, Outstanding Doctoral Dissertation in Bayesian Statistics, International Society for Bayesian Analysis2010
National Science Foundation Graduate Research Fellowship2006
Marshall Scholarship for study in Great Britain2004

Page last updated: 10/21/2014