Publications: Dawn Woodard
D. B. Woodard, G. Nogin, P. Koch, D. Racz, M. Goldszmidt, and E. Horvitz (2017). Predicting travel time reliability using mobile phone GPS data. Transportation Research Part C, 75: 30-44. pdf
B. S. Westgate, D. B. Woodard, D. S. Matteson, and S. G. Henderson (2016). Large-network travel time distribution estimation for ambulances. European Journal of Operational Research, 252:322-333. pdf.
L. Bornn, N. Pillai, A. Smith, and D. Woodard (2016). The use of a single pseudo-sample in Approximate Bayesian Computation MCMC. Statistics and Computing. doi:10.1007/s11222-016-9640-7. arXiv:1404.6298.
Z. Zhou, D.S. Matteson, D.B. Woodard, S.G. Henderson and A.C. Micheas (2015). A spatio-temporal
point process model for ambulance demand. Journal of
the American Statistical Association, 110:6-15. Winner of the 2014 American Statistical Association Health Policy Statistics Student Paper Competition and Finalist in the 2013 INFORMS Data Mining Student Paper Competition. pdf
D. Singhvi, S. Singhvi, P.I. Frazier, S.G. Henderson, E. O'Mahony, D.B. Shmoys, and D.B. Woodard (2015). Predicting bike usage for New York City's bike sharing system. AAAI 2015 Workshop on Computational Sustainability. pdf.
D.B. Woodard, C. Crainiceanu, and D. Ruppert (2013). Hierarchical adaptive regression kernels for regression with functional predictors.
J. of Computational and Graphical Statistics, 22: 777-800. published; preprint; web appendix.
D. B. Woodard and J. S. Rosenthal (2013). Convergence rate of Markov chain methods for genomic motif discovery.
Annals of Statistics, 41: 91-124. published (pdf); web appendix;
D. B. Woodard, T. M. T. Love, S. W. Thurston, D. Ruppert, S. Sathyanarayana, and S. H. Swan (2013).
Latent factor regression models for grouped outcomes. Biometrics, 69: 785-794. published;
B. S. Westgate, D. B. Woodard, D. S. Matteson, and S. G. Henderson (2013). Travel time estimation
for ambulances using Bayesian data augmentation. Annals of Applied Statistics, 7: 1139-1161. published (pdf);
web appendix; journal website.
D. Woodard. Comment on article by Schmidl et al. (2013). Bayesian Analysis, 8:23-26. published pdf.
D.B. Woodard and M. Goldszmidt (2011). Online model-based clustering
for crisis identification in distributed computing. J. of the American Statistical Association, 106:49-60.
published, preprint (pdf).
D.B. Woodard, D. S. Matteson and S. G. Henderson (2011). Stationarity of generalized autoregressive moving average models. Electronic Journal of Statistics, 5:800-828. published.
D.S. Matteson, M.W. McLean, D.B. Woodard, and S.G. Henderson (2011). Forecasting Emergency Medical Service call arrival rates.
Annals of Applied Statistics, 5:1379-1406. published (pdf); Journal website.
M. Goldszmidt, D.B. Woodard and P. Bodik (2011). Real-time identification of performance problems
in large distributed systems. In A. Srivastava and J. Han, ed., Machine Learning and Knowledge
Discovery for Engineering Systems Health Management. Boca Raton, FL: Taylor and Francis. 502 pp.
D.B. Woodard, R.L. Wolpert and M.A. O'Connell (2010). Spatial inference of nitrate
concentrations in groundwater. J. of Agricultural, Biological, and Environmental Statistics, 15:209-227.
published, preprint (pdf) .
P. Bodik, M. Goldszmidt, A. Fox, D.B. Woodard and H. Andersen (2010). Fingerprinting the datacenter:
Automated classification of performance crises. In G. Muller, editor, EuroSys 2010: Proc. of the 5th
European Conference on Computer Systems, pp.111-124. New York: Association for Computing Machinery.
D.B. Woodard, S.C. Schmidler,
M.L. Huber (2009). Conditions for rapid mixing of parallel and simulated
tempering on multimodal distributions. Annals of
Applied Probability, 19:617-640. published (pdf), Journal website.
D.B. Woodard, S.C. Schmidler, M.L. Huber (2009). Sufficient conditions
for torpid mixing of parallel and simulated tempering. Electronic Journal of Probability, 14:780-804.
D.B. Woodard, A. E. Gelfand, W. E. Barlow, and J. G. Elmore
(2007). Performance assessment for radiologists interpreting screening
mammography. Statistics in Medicine, 26:1532-1551.
published, preprint (pdf).
D.B. Woodard (2007).
Conditions for rapid and torpid mixing of parallel and simulated tempering on
multimodal distributions. Thesis, Duke U. pdf
D.B. Woodard, J. Hoffman, and A. Jack (2007). Bayesian modeling with
S-PLUS and the S+flexBayes library. In D. Spruck, ed.,
Proceedings of the Pharmaceutical Users Software Exchange Conference, #ST07, 11 pages. Kent, U.K.: Pharmaceutical Users Software Exchange. Microsoft Word (.doc) , postscript (.ps)
S.C. Schmidler and D. B. Woodard. Lower bounds on the convergence rates of adaptive MCMC methods. Under invited revision for Annals of Statistics. pdf (Updated 03/12/2013).
Originally published as an ORIE Technical report in Jan 2011.
D. B. Woodard, R. Bilina Falafala, and C. Crainiceanu. Model-based image segmentation via Monte Carlo EM, with application to DCE-MRI. Under revision for Journal of Computational and Graphical Statistics.
pdf, web appendix.
D.B. Woodard (2014). A lower bound on the mixing time of uniformly ergodic Markov chains in terms of the spectral radius. ArXiv technical report arXiv:1405.0028.
D.B. Woodard (2008). Detecting poor mixing of posterior samplers due to
multimodality. Technical report, Duke University Department of Statistical Science. Updated Feb. 2011. pdf