Publications

Peer-Reviewed Manuscripts

Brown, D. A., Lazar, N. A., Datta, G. S., Jang, W., and McDowell, J. E. (2014), “Incorporating spatial dependence into Bayesian multiple testing of statistical parametric maps in functional neuroimaging,” NeuroImage, 84, 97-112.

Brown, D. A., Datta, G. S., and Lazar, N. A. (2017), “A Bayesian generalized CAR model for correlated signal detection,” Statistica Sinica, 27, 1125-1153. (R Code (.zip))

Brown, D. A. and Atamturktur, S. (2018), “Nonparametric functional calibration of computer models,” Statistica Sinica, 28, 721-742 (Matlab Code (.zip))

Stevens, G. N., Atamturktur, S., Brown, D. A., Williams, B. J., and Unal, C. (2018), “Statistical inference of empirical constituents in partitioned analysis from integral-effect experiments: An application to thermo-mechanical coupling,” Engineering Computations, 35, 672-691.

Brown, D. A., Saibaba, A., and Vallélian, S. (2018), “Low rank independence samplers in hierarchical Bayesian inverse problems,” SIAM/ASA Journal on Uncertainty Quantification, 6, 1076-1100. (PDF / Supplement)

Self, S. W.,  McMahan, C., Brown, D. A., Lund, R., Gettings, J., and Yabsley, M. (2018), “A large scale spatio-temporal binomial regression model for estimating seroprevalence trends,” Environmetrics, to appear, DOI: 10.1002/env.2538arXiv preprint

Prabhu, S., Ehrett, C., Javanbarg, M., Brown, D. A.,  Lehmann, M., and Atamturktur, S., “Uncertainty quantification in fault tree analysis: Estimating business interruption due to seismic hazard,” submitted.

Brown, D. A., McMahan, C. S., Shinohara, R. T., and Linn, K. L., “Bayesian spatial binary regression for label fusion in structural neuroimaging,” under revision. arXiv preprint

Brown, D. A., McMahan, C. S., and Watson, S. C., “Sampling strategies for fast updating of Gaussian Markov random fields,” under revision. arXiv preprint (R Code (.zip))

Yan, Z., Brown, D. A., Nagatomi, J., and Mefford, O. T., “Synthesis and characterization of the thermoresponsive properties of 4-arm poloxamines and construction of a corresponding empirical model,” under revision.

Flynn, G. S., Chodora, E., Atamturktur, S., and Brown, D. A., “A Bayesian inference-based approach to empirical training of strongly-coupled constituent models,” submitted.

Saibaba, A. K., Bardsley, J., Brown, D. A., and Alexenderian, A., “Efficient marginalization-based MCMC methods for hierarchical Bayesian inverse problems,” submitted. arXiv preprint

 

Peer-Reviewed Proceedings

Atamturktur, S. and Brown, D. A. (2015), “State-aware calibration for inferring systematic bias in computer models of complex systems,” NAFEMS World Congress 2015, June 21-24, San Diego, CA, ISBN 978-1-910643-24-2.

Gallagher, E., Brown, C. J., Brown, D. A.,  Frady, K., Marcanikova, M., Atamturktur, S., Ihekweazu, S., Matthews, M., Rabb, R., Roberts, R., Solan, I., Welch, R., and Gramopadhye, A. K. (2018), “Statewide coalition: Supporting underrepresented populations in precalculus through organizational redesign toward engineering diversity (SC:SUPPORTED) year 1,” Proceedings of the 2018 American Society of Engineering Education Annual Conference and Exhibition, June 24-27, Salt Lake City, UT.

Gallagher, E., Brown, D. A., Brown, C. J., , Frady, K., Bass, P., Matthews, M., Peters, T., Rabb, R., Solan, I., Welch, R., and Gramopadhye, A. (2018), “Identifying mathematical pathways to engineering in South Carolina,” Proceedings of the 2018 American Society of Engineering Education Annual Conference and Exhibition, June 24-27, Salt Lake City, UT.  

 

Letters and Discussions

Brown, D. A. and Lazar, N. A. (2018), Discussion of “Bayesian spatiotemporal modeling using hierarchical spatial priors, with applications to functional magnetic resonance imaging,” by M. Bezener, J. Hughes, and G. Jones, Bayesian Analysis, to appear. (Link to advance publication)

 

Book Chapters

Atamturktur, S., Stevens, G. N, and Brown, D. A. (2017), “Empirically improving model adequacy in scientific computing,” in Model Validation and Uncertainty Quantification, Volume 3: Proceedings of the 35th IMAC, A Conference and Exposition of Structural Dynamics 2017eds. Barthorpe, R., Platz, R., Lopez, I., Moaveni, B., and Papadimitriou, C., pp. 363-370

 

Book Reviews

Brown, D. A. (2017), Review of Analysis of Neural Data, by R. E. Kass, U. T. Eden, and E. N. Brown, Biometrics, 73, 710-713.

 

Unreviewed Proceedings

Jaeger, A., Brown, D. A., Seymour, L., and Beuckert, R. (2010), “Response of Canadian crop yields to climate change,” in Proceedings of the 2010 Joint Statistical Meetings, Statistics and the Environment Section, Alexandria: American Statistical Association, pp. 4395 – 4405.

Brown, D. A., Lazar, N. A., and Datta, G. S. (2011), “Bayesian multiple testing under dependence with application to functional magnetic resonance imaging,” in Proceedings of the 2011 Joint Statistical Meetings, Bayesian Statistical Science Section, Alexandria:American Statistical Association, pp. 4708 – 4722.

Stevens, G. N., Atamturktur, S., and Brown, D. A. (2017), “Empirical training of constituent models: Defining meso-scale behavior in a multi-scale plasticity model,” IMAC XXXV, Society for Experimental Mechanics, Jan. 30 – Feb. 2, Garden Grove, CA.

 

Manuscripts in Preparation

Self, S. W., McMahan, C. S., and Brown, D. A., “A Bayesian multi-dimensional trend filter,” in preparation.

Ehrett, C., Atamturktur, S., Brown, D. A., Chodora, E., Jiang, M., and Kitchens, C., “Computer model calibration as a method of design,” in preparation.

Mokalled, S., McMahan, C. S., Brown, D. A., Tebbs, J. M., and Bilder, C. R., “Acknowledging the dilution effect in group testing data: A new approach,” in preparation.