Documents
] "Visualizing uncertainty in areal data estimates with bivariate choropleth maps, map pixelation, and glyph rotation." Stat 6, no. 1 (2017): 292-302, available at http://onlinelibrary.wiley.com/doi/10.1002/sta4.150/abstract.
"A stochastic bioenergetics model based approach to translating large river flow and temperature in to fish population responses: the pallid sturgeon example." Geological Society 408 (2015). DOI: 10.1144/SP408.10.
"Small Area Estimation via Multivariate Fay-Herriot Models With Latent Spatial Dependence." Australian & New Zealand Journal of Statistics 57 (2015): 15-29, available at http://arxiv.org/abs/1310.7211.
"Semiparametric Dynamic Design of Monitoring Networks for Non-Gaussian Spatio-Temporal Data." In Spatio-temporal Design: Advances in Efficient Data Acquisition, edited by Jorge Mateu and Werner Muller, 269-284. Chichester, UK: Wiley, 2012, available at http://onlinelibrary.wiley.com/doi/10.1002/9781118441862.ch12/summary.
"Regionalization of Multiscale Spatial Processes using a Criterion for Spatial Aggregation Error." Journal of the Royal Statistical Society -- Series B. (2017), available at https://arxiv.org/abs/1502.01974.
"Regionalization of Multiscale Spatial Processes using a Criterion for Spatial Aggregation Error." ArXiv, no. 1502.01974 (2015), available at http://arxiv.org/abs/1502.01974.
"Nonlinear Dynamic Spatio-Temporal Statistical Models." In Southern Regional Council on Statistics Summer Research Conference., 2013.
"Multivariate Spatio-Temporal Models for High-Dimensional Areal Data with Application to Longitudinal Employer-Household Dynamics." Annals of Applied Statistics 9, no. 4 (2015). DOI: 0.1214/15-AOAS862.
"Multivariate Spatio-Temporal Models for High-Dimensional Areal Data with Application to Longitudinal Employer-Household Dynamics." ArXiv, no. 1503.00982 (2015), available at http://arxiv.org/abs/1503.00982.
"Multivariate Spatial Hierarchical Bayesian Empirical Likelihood Methods for Small Area Estimation." STAT 4, no. 1 (2015): 108-116. DOI: 10.1002/sta4.81, available at http://dx.doi.org/10.1002/sta4.81.
Multiscale Analysis of Survey Data: Recent Developments and Exciting Prospects, Statistics Views., 2015.
"Modern Perspectives on Statistics for Spatio-Temporal Data." WIRES Computational Statistics 7, no. 1 (2015): 86-98. DOI: 10.1002/wics.1341, available at http://dx.doi.org/10.1002/wics.1341.
"Hierarchical, stochastic modeling across spatiotemporal scales of large river ecosystems and somatic growth in fish populations under various climate models: Missouri River sturgeon example." Geological Society (2015).
"Hierarchical Spatial Models." In Encyclopedia of Geographical Information Science. Springer, 2015.
"Hierarchical General Quadratic Nonlinear Models for Spatio-Temporal Dynamics." In Red Raider Conference. Lubbock, TX: Texas Tech University, 2012.
"Hierarchical Dynamic Generalized Linear Mixed Models for Discrete--Valued Spatio-Temporal Data." In Handbook of Discrete--Valued Time Series., 2015.
"Hierarchical Dynamic Generalized Linear Mixed Models for Discrete-Valued Spatio-Temporal Data." In Handbook of Discrete-Valued Time Series, edited by R. Davis, S. Holan, R. Lund and N Ravishanker. Boca Raton, FL: Chapman and Hall/CRC Press, 2015, available at http://www.crcpress.com/product/isbn/9781466577732.
"Hierarchical Bayesian Spatio-Temporal Conway-Maxwell Poisson Models with Dynamic Dispersion." Journal of Agricultural, Biological, and Environmental Statistics 18 (2013): 335-356. DOI: 10.1007/s13253-013-0141-2, available at http://link.springer.com/article/10.1007/s13253-013-0141-2.
"Hierarchical Agent-Based Spatio-Temporal Dynamic Models for Discrete Valued Data." In Handbook of Discrete-Valued Time Series, edited by R. Davis, S. Holan, R. Lund and N. Ravishanker. Boca Raton, FL.: Chapman and Hall/CRC Press, 2015, available at http://www.crcpress.com/product/isbn/9781466577732.
"Hierarchcial models for uncertainty quantification: An overview." In Handbook of Uncertainty Quantification, edited by Ghanem, R., Higdon, D. and Owhadi, H.. Springer, 2015.
"Generating Partially Synthetic Geocoded Public Use Data with Decreased Disclosure Risk Using Differential Smoothing." Journal of the Royal Statistical Society - Series A (2016), available at https://arxiv.org/abs/1507.05529.