Peer-Reviewed Manuscripts

Sakitis, C., Brown, A., and Rowe, D., “Increased accuracy in statistical analysis of task activation with a formal Bayesian approach to SENSE image reconstruction,” submitted.

Niu, J., Hur, B., Absher, J., and Brown, D. A., “Bayesian regularization for functional graphical models,” under revision. arXiv preprint

Nicholson, J., Kiessler, P., and Brown, D. A., “A kernel-based approach for Gaussian process modeling with functional information,” under revision. arXiv preprint

Brown, D. A., McMahan, C. S., Shinohara, R. T., and Linn, K. L. (2022), “Bayesian spatial binary regression for label fusion in structural neuroimaging,” Journal of the American Statistical Association, 117, 547-560.

Mokalled, S., McMahan, C., Tebbs, J., Brown, D. A., and Bilder, C. (2021), “Incorporating the dilution effect into group testing regression,” Statistics in Medicine, 40, 2540-2555.

Ehrett, C., Brown, D. A., Kitchens, C., Xu, X., Platz, R., and Atamturktur, S. (2021), “Simultaneous Bayesian calibration and engineering design with application to a vibration isolation system,” ASME Journal of Verification, Validation, and Uncertainty Quantification, 6:011007.

Brown, D. A., McMahan, C. S., and Self, S. W. (2021), “Sampling strategies for fast updating of Gaussian Markov random fields,” The American Statistician, 75, 52-65. (R Code (.zip))

Ehrett, C., Brown, D. A., Chodora, E., Kitchens, C., and Atamturktur, S. (2021), “Multi-objective engineering design via computer model calibration,” ASME Journal of Mechanical Design, 143:051702.

Gettings, J. R., Self, S. W., McMahan, C. S., Brown, D. A., Nordone, S. K., and Yabsley, M. J. (2020), “Regional and local temporal trends of Borrelia burgdorferi and Anaplasma spp. seroprevalence in domestic dogs: contiguous United States 2013-2019,” Frontiers in Veterinary Science, 7:561592.

Gettings, J., Self, S. C. W., McMahan, C. S., Brown, D. A., Nordone, S. K., and Yabsley, M. J. (2020), “Local and regional temporal trends (2013-2019) of canine Ehrlichia spp. seroprevalence in the United States,” Parasites and Vectors, 13:153.

Prabhu, S., Ehrett, C., Javanbarg, M., Brown, D. A.,  Lehmann, M., and Atamturktur, S. (2020), “Uncertainty quantification in fault tree analysis: Estimating business interruption due to seismic hazard,” Natural Hazards Review, 21:04020015.

Flynn, G. S., Chodora, E., Atamturktur, S., and Brown, D. A. (2019), “A Bayesian inference-based approach to empirical training of strongly-coupled constituent models,” ASME Journal of Verification, Validation, and Uncertainty Quantification, 4:021005.

Saibaba, A. K., Bardsley, J., Brown, D. A., and Alexanderian, A. (2019), “Efficient marginalization-based MCMC methods for hierarchical Bayesian inverse problems,” SIAM/ASA Journal on Uncertainty Quantification, 7, 1105-1131.

Self, S. W., Pulaski, C. N., McMahan, C. S., Brown, D. A., Yabsley, M. J., and Gettings, J. (2019), “Regional and local temporal trends in the prevalence of canine heartworm infection in the contiguous United States: 2012-2018,” Parasites and Vectors, 12:380.

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, 29:e2538.

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)

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

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., 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

Peer-Reviewed Proceedings

Gallagher, E., Vagnozzi, A. M., Lanning, R.,  Brown, D., Brown, C., Frady, K.,  Brisbane, J.,  Matthews, M., Murphy, J., Patel, K., Pfirman, A., Rabb, R., Roberts, R., Welch, R., and Gramopadhye, A. (2020), “Poverty and guidance: Challenges and opportunities in mathematics preparation for engineering,” Proceedings of the 2020 American Society of Engineering Education Annual Conference and Exhibition, June 21 – 24, Montreal, Canada.

Marcanikova, M., Gallagher, E., Brown, C., Brisbane, J., Brown, A., Dunwoody, L. A., Frady, K., Hines, A., Murphy, J., Patel, K., Pfirman, A., Roberson, S., and Gramopadhye, A. (2019), “High school technology as a NON-predictor of first college math course,” Proceedings of the 2019 American Society of Engineering Education Southeast Section Conference, March 10-12, Raleigh, NC.

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.  

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.

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.

Letters and Discussions

Brown, D. A. (2022), Discussion of “Deep Gaussian processes for calibration of computer models,” by S. Marmin and M. Filippone, Bayesian Analysis, 17, 1342-1343.

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, 13, 1307-1308.

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 2017, eds. 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

Sakitis, C. J., Rowe, D. B., and Brown, D. A. (2021), “A formal Bayesian approach to SENSE image reconstruction,” in Proceedings of the 2021 Joint Statistical Meetings, Section on Statistics in Imaging.

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.

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.

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.