| Nonparametric functional calibration of computer models DA Brown, S Atamturktur Statistica Sinica, 721-742, 2018 | 41 | 2018 |
| Regional and local temporal trends in the prevalence of canine heartworm infection in the contiguous United States: 2012–2018 SW Self, CN Pulaski, CS McMahan, DA Brown, MJ Yabsley, JR Gettings Parasites & vectors 12, 1-10, 2019 | 32 | 2019 |
| Incorporating spatial dependence into Bayesian multiple testing of statistical parametric maps in functional neuroimaging DA Brown, NA Lazar, GS Datta, W Jang, JE McDowell NeuroImage 84, 97-112, 2014 | 29 | 2014 |
| Local and regional temporal trends (2013–2019) of canine Ehrlichia spp. seroprevalence in the USA JR Gettings, SCW Self, CS McMahan, DA Brown, SK Nordone, ... Parasites & vectors 13, 1-11, 2020 | 21 | 2020 |
| Low-rank independence samplers in hierarchical Bayesian inverse problems DA Brown, A Saibaba, S Vallélian SIAM/ASA Journal on Uncertainty Quantification 6 (3), 1076-1100, 2018 | 19* | 2018 |
| A large‐scale spatio‐temporal binomial regression model for estimating seroprevalence trends SCW Self, CS McMahan, DA Brown, RB Lund, JR Gettings, MJ Yabsley Environmetrics 29 (8), e2538, 2018 | 17 | 2018 |
| Sampling strategies for fast updating of Gaussian Markov random fields DA Brown, CS McMahan, SW Self The American Statistician, 2019 | 16 | 2019 |
| Efficient marginalization-based MCMC methods for hierarchical Bayesian inverse problems AK Saibaba, J Bardsley, DA Brown, A Alexanderian SIAM/ASA Journal on Uncertainty Quantification 7 (3), 1105-1131, 2019 | 15 | 2019 |
| Regional and Local Temporal Trends of Borrelia burgdorferi and Anaplasma spp. Seroprevalence in Domestic Dogs: Contiguous United States 2013–2019 JR Gettings, SCW Self, CS McMahan, DA Brown, SK Nordone, ... Frontiers in Veterinary Science 7, 561592, 2020 | 11 | 2020 |
| Statistical inference of empirical constituents in partitioned analysis from integral-effect experiments: An application in thermo-mechanical coupling GN Stevens, S Atamturktur, DA Brown, BJ Williams, C Unal Engineering Computations 35 (2), 672-691, 2018 | 8 | 2018 |
| State-aware calibration for inferring systematic bias in computer models of complex systems S Atamturktur, DA Brown NAFEMS World Congress Proceedings, San Diego, CA, 2015 | 8 | 2015 |
| Incorporating the dilution effect in group testing regression SC Mokalled, CS McMahan, JM Tebbs, D Andrew Brown, CR Bilder Statistics in Medicine 40 (11), 2540-2555, 2021 | 5 | 2021 |
| Uncertainty quantification in fault tree analysis: estimating business interruption due to seismic hazard S Prabhu, C Ehrett, M Javanbarg, DA Brown, M Lehmann, S Atamturktur Natural Hazards Review 21 (2), 04020015, 2020 | 5 | 2020 |
| A Bayesian generalized CAR model for correlated signal detection DA Brown, GS Datta, NA Lazar Statistica Sinica, 1125-1153, 2017 | 5 | 2017 |
| A kernel-based approach for modelling Gaussian processes with functional information J Nicholson, P Kiessler, DA Brown arXiv preprint arXiv:2201.11023, 2022 | 4 | 2022 |
| Simultaneous Bayesian calibration and engineering design with an application to a vibration isolation system C Ehrett, DA Brown, C Kitchens, X Xu, R Platz, S Atamturktur Journal of Verification, Validation and Uncertainty Quantification 6 (1), 011007, 2021 | 3 | 2021 |
| Inferring effective interphase properties in composites by inverse analysis JC Arp, J Nicholson, J Geddes, DA Brown, S Atamturktur Russcher, ... ACS Applied Polymer Materials 5 (10), 7733-7745, 2023 | 2 | 2023 |
| Bayesian spatial binary regression for label fusion in structural neuroimaging DA Brown, CS McMahan, RT Shinohara, KA Linn, ... Journal of the American Statistical Association 117 (538), 547-560, 2022 | 2 | 2022 |
| Identifying Prevalent Mathematical Pathways to Engineering in South Carolina E Gallagher, C Brown, DA Brown, KK Frady, P Bass, MA Matthews, ... | 2 | 2018 |
| Analysis of Neural Data DA Brown Biometrics 73 (2), 710-713, 2017 | 2 | 2017 |
Christopher McmahanClemson UniversityDirección de correo verificada de g.clemson.edu
