James Scott
Professor
Department: Information, Risk & Operations Management
Additional Titles: Associate Professor of Statistics
Industry Areas: Business Statistics, Financial Risk Management
Research Areas: Bayesian Methods, Decision Theory, Probability and Statistics, Risk Management, Statistical Analysis
James Scott received a B.S. in mathematics from the University of Texas, a masters 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.
ACADEMIC LEADERSHIP & AWARDS
2010 |
Professional Awards Savage Award, Outstanding Doctoral Dissertation in Bayesian Statistics, International Society for Bayesian Analysis
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2006 |
National Science Foundation Graduate Research Fellowship
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2004 |
Marshall Scholarship for study in Great Britain
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Publications
Evaluation of E-scooters as Transit Last-mile Solution. By: Natalia Zuniga-Garcia, Mauricio Tec, James G. Scott, and Randy B. Machemehl. Transportation Research: Part C. Jun2022. Vol. 139: 103660
Optimal Post-selection Inference for Sparse Signals: A Nonparametric Empirical Bayes Approach. By: S. Woody, O. H. M. Padilla, James G. Scott. Biometrika. Feb2022. Vol. 109(1): 1-16
Tyler Buffington, James Scott, and Ofodike A. Ezekoye. 2021. Combining Spatial and Sociodemographic Regression Techniques to Predict Residential Fire Counts at the Census Tract Level. Computers, Environment and Urban Systems 88: 101633.
Abigail R. A. Aiken, Jennifer E. Starling, Alexandra van der Wal, Sascha van der Vliet, Kathleen Broussard, Dana M. Johnson, Elisa Padron, Rebecca Gomperts, and James Scott. 2020. Demand for Self-Managed Medication Abortion through an Online Telemedicine Service in the United States. American Journal of Public Health 110(1), 90-97.
Natalia Zuniga-Garcia, Mauricio Tec, James Scott, Natalia Ruiz-Juir, and Randy B. Machemehl. 2020. Evaluation of Ride-sourcing Search Frictions and Driver Productivity: A Spatial Denoising Approach. Transportation Research: Part C 110, 346-367.
Oscar Hernan Madrid Padilla, Alex Athey, Alex Reinhart, and James Scott. 2019. Sequential Nonparametric Tests for a Change in Distribution: An Application to Detecting Radiological Anomalies. Journal of the American Statistical Association 144(526), 514-528.
Nick Polson and James Scott. 2018. AIQ: How People and Machines are Smarter Together. NY, NY: St. Martin's Press.
Wesley Tansey, Oluwasanmi Koyejo, Russell A. Poldrack, and James Scott. 2018. False Discovery Rate Smoothing. Journal of the American Statistical Association 113(523), 1156-1171.
Wesley Tansey, Alex Athey, Alex Reinhart, and James Scott. 2017. Multiscale Spatial Density Smoothing: An Application to Large-Scale Radiological Survey and Anomaly Detection. Journal of the American Statistical Association 112(519), 1047-1063.
Nicholas G. Polson and James Scott. 2016. Mixtures, Envelopes and Hierarchical Duality. Journal of the Royal Statistical Society: Series B (Statistical Methodology) 78(4), 701-727.
Prasad Buddhavarapu, James Scott, and Jorge A. Prozzi. 2016. Modelling Unobserved Heterogeneity Using Finite Mixture Random Parameters for Spatially Correlated Discrete Count Data. Transportation Research: Part B 91, 492-510.
Mingyuan Zhou, Oscar Hernan Madrid, and James Scott. 2016. Priors for Random Count Matrices Derived from a Family of Negative Binomial Processes. Journal of the American Statistical Association 111(515), 1144-1156.
James Scott, Ryan C. Kelly, Matthew A. Smith, Pengcheng Zhou, and Robert E. Cass. 2015. False Discovery Rate Regression: An Application to Neural Synchrony Detection in Primary Visual Cortex. Journal of the American Statistical Association 110, 459-471.
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.