Spatio-Temporal Statistics with R

2024

Bertolacci, M., Zammit-Mangion, A., Schuh, A., Bukosa, B., Fisher, J. A., Cao, Y., Kaushik, A., and Cressie, N. (2024). Inferring changes to the global carbon cycle with WOMBAT v2.0, a hierarchical flux-inversion framework. Annals of Applied Statistics, 18, 303–327 (doi:10.1214/23-AOAS1790).

Bonas, M., Wikle, C.K., and Castruccio, S. (2024). Calibrated forecasts of quasi-periodic climate processes with deep echo state networks and penalized quantile regression. Environmetrics, 35, e2833 (doi:10.1002/env.2833).

Grieshop, N. and Wikle, C.K. (2024). Data-driven modeling of wildfire spread with stochastic cellular automata and latent spatio-temporal dynamics. Spatial Statistics, 59, 100794 (doi:10.1016/j.spasta.2023.100794).

Grieshop, N. and Wikle, C.K. (2024). Echo state network-enhanced symbolic regression for spatio-temporal binary stochastic cellular automata. Spatial Statistics, 60, 100827 (doi:10.1016/j.spasta.2024.100827).

North, J.S., Wikle, C.K., and Schliep, E.M. (2024). A Bayesian approach for spatio-temporal data-driven dynamic equation discovery. Bayesian Analysis (doi:10.1214/23-BA1406).

Pearse, A. R., Cressie, N., and Gunawan, D. (2024). Optimal prediction of positive-valued spatial processes: Asymmetric power-divergence loss. Spatial Statistics, 60, 100829 (doi:10.1016/j.spasta.2024.100829).

Sainsbury-Dale, M., Zammit-Mangion, A., and Cressie, N. (2024). Modeling big, heterogeneous, non-Gaussian spatial and spatio-temporal data using FRK. Journal of Statistical Software, 108(10), 1- 39 (doi:10.18637/jss.v108.i10).

Sainsbury-Dale, M., Zammit-Mangion, A., and Huser, R. (2024). Likelihood-free parameter estimation with neural Bayes estimators. The American Statistician, 78, 1-14 (doi:10.1080/00031305.2023.2249522).

Yoo, M. and Wikle, C.K. (2024). A Bayesian spatio-temporal level set dynamical model and application to fire front propagation. Annals of Applied Statistics, 18, 404-423 (doi:10.1214/23-AOAS1794).

Zammit-Mangion, A., Kaminski, M. D., Tran, B-H., Filippone, M., and Cressie, N. (2024). Spatial Bayesian neural networks. Spatial Statistics, 60, 100825 (doi:10.1016/j.spasta.2024.100825).

2023

Berliner, L.M., Herbei, R., Wikle, C.K., and Milliff, R.F. (2023). Excursions in the Bayesian treatment of model error. PLoS ONE, 18, e0286624 (doi:10.1371/journal.pone.0286624).

Byrne, B. et al. (with 60 co-authors including Cressie, N. and Zammit-Mangion, A.). (2023). National CO₂ budgets (2015-2020) inferred from atmospheric CO₂ observations in support of the global stocktake. Earth System Science Data, 15, 963-1004 (doi:10.5194/essd-15-963-2023).

Cressie, N. (2023). Adapting statistical science for a fast-changing climate. CHANCE, 36.1, 9-13 (doi:10.1080/09332480.2023.2179263).

Cressie, N. (2023). Decisions, decisions, decisions in an uncertain environment. Environmetrics, 34, e2767 (doi:10.1002/env.2767).

Cressie, N. and Moores, M. T. (2023). Spatial statistics, in Encyclopedia of Mathematical Geosciences, eds B. S. Daya Sagar, Q. Cheng, J. McKinley, and F. Agterberg. Springer, Cham, CH, pp.1362-1373 (doi:10.1007/978-3-030-26050-7_31-2).

Cressie, N., Zammit-Mangion, A., Jacobson, J., and Bertolacci, M. (2023). Earth’s CO₂ battle: a view from space. Significance, 20, February 2023 issue, pp.14-19 (doi:10.1093/jrssig/qmad003).

Daw, R. and Wikle, C.K. (2023). REDS: Random ensemble deep spatial prediction. Environmetrics, 34, e2780 (doi:110.1002/env.2780).

Jacobson, J., Cressie, N., and Zammit-Mangion, A. (2023). Spatial statistical prediction of solar-induced chlorophyll fluorescence (SIF) from multivariate OCO-2 data. Remote Sensing, 15, 4038 (doi:10.3390/rs15164038).

Ng, T.L.J. and Zammit-Mangion, A. (2023). Non-homogeneous Poisson process intensity modelling and estimation using measure transport. Bernoulli, 29, 815-838 (doi:10.3150/22-BEJ1480).

North, J.S., Wikle, C.K., and Schliep, E.M. (2023). A review of data-driven discovery for dynamic systems. International Statistical Review, 91, 464-492 (doi:10.1111/insr.12554).

Schliep, E., Wikle, C.K., and Daw, R. (2023). Correcting for informative sampling in spatial covariance estimation and kriging predictions. Journal of Geographical Systems, 25, 587-613 (doi:10.1007/s10109-023-00426-9).

Simpson, M., Holan, S.H., Wikle, C.K., and Bradley, J.R. (2023). Interpolating population distributions using public-use data: An application to income segregation using American Community Survey data. Journal of the American Statistical Association, 118, 84-96 (doi:10.1080/01621459.2022.2126779).

Vu., Q., Zammit-Mangion, A., and Chuter, S. (2023). Constructing large nonstationary spatio-temporal covariances via compositional warpings. Spatial Statistics, 54, 100742 (doi:10.1016/j.spasta.2023.100742).

Wikle, C.K. and Zammit-Mangion, A. (2023). Statistical deep learning for spatial and spatio-temporal data. Annual Review of Statistics and Its Application, 10, 247-270 (doi:10.1146/annurev-statistics-033021-112628).

Wikle, C.K., Mateu, J., and Zammit-Mangion, A. (2023). Deep learning and spatial statistics. Spatial Statistics, 57, 100774 (doi:10.1016/j.spasta.2023.100774).

Yoo, M. and Wikle, C.K. (2023). Using echo state networks to inform physical models for fire front propagation. Spatial Statistics, 54, 100732 (doi:10.1016/j.spasta.2023.100732).

2022

Cressie, N., Pearse, A., and Gunawan, D. (2022). Optimal spatial prediction for non-negative spatial processes using a phi-divergence loss function, in Trends in Mathematical, Information, and Data Sciences, eds N. Balakrishnan, M. A. Gil, N. Martin, D. Morales, and M. Pardo. Springer, Cham, CH, pp. 181-197 (doi:10.1007/978-3-031-04137-2_17).

Cressie, N., Sainsbury-Dale, M., and Zammit-Mangion, A. (2022). Basis-function models in spatial statistics. Annual Review of Statistics and its Application, 9, 373-400 (doi:10.1146/annurev-statistics-040120-020733).

Daw, R. and Wikle, C.K. (2022). Supervised spatial regionalization using the Karhunen-Loève expansion and minimum spanning trees. Journal of Data Science, 20, 566–584 (doi:110.6339/22-JDS1077).

Gopalan, G. and Wikle, C.K. (2022). A multi-surrogate higher-order singular value decomposition tensor emulator for spatio-temporal simulators. Journal of Agricultural, Biological and Environmental Statistics, 27, 22–45 (doi:10.1007/s13253-021-00459-x).

North, J.S., Wikle, C.K., and Schliep, E.M. (2022). A Bayesian approach for data-driven dynamic equation discovery. Journal of Agricultural, Biological and Environmental Statistics, 27, 728–747 (doi:10.1007/s13253-022-00514-1).

Schafer, T.L.J., Wikle, C.K., and Hooten, M.B. (2022) Bayesian inverse reinforcement learning for collective animal movement. Annals of Applied Statistics, 16, 999-1013 (doi:10.1214/21-AOAS1529).

Vu, Q., Zammit-Mangion, A., and Cressie, N. (2022). Modeling nonstationary and asymmetric multivariate spatial covariances via deformations. Statistica Sinica, 32, 2071-2093 (doi:10.5705/ss.202020.0156).

Wikle, C.K., Datta, A., Hari, B.V., Boone, E.L., Sahoo, I., Kavila, I., Castruccio, S., Simmons, S.J., Burr, W.S., and Chang, W. (2022). An illustration of model agnostic explainability methods applied to environmental data. Environmetrics, 34, e2772 (doi:110.1002/env.2772).

Zammit-Mangion, A., Bertolacci, M., Fisher, J., Stavert, A., Rigby, M., Cao, Y., and Cressie, N. (2022). WOMBAT v1.0: a fully Bayesian global flux-inversion framework. Geoscientific Model Development, 15, 45-73 (doi:10.5194/gmd-15-45-2022).

Zammit-Mangion, A., Ng, T.L.J., Vu, Q., and Filippone, M. (2022). Deep compositional spatial models. Journal of the American Statistical Association, 117, 1787-1808 (doi:10.1080/01621459.2021.1887741).

Zhang, B., Li, F., Sang, H., and Cressie, N. (2022). Inferring changes in Arctic sea ice through a spatio-temporal logistic autoregression fitted to remote-sensing data. Remote Sensing, 14, 5995 (doi:10.3390/rs14235995).

2021

Cressie, N. (2021). A few statistical principles for data science. Australian & New Zealand Journal of Statistics, 63, 182-200 (doi:10.1111/anzs.12324).

Cressie, N. and Wikle, C. K. (2021). Modeling dependence in spatio-temporal econometrics, in Advances in Contemporary Statistics and Econometrics, eds A. Daouia and A. Ruiz-Gazen. Springer, Cham, CH, pp. 363-383 (doi:10.1007/978-3-030-73249-3_19).

Huang, H.-C., Cressie, N., Zammit-Mangion, A., and Huang, G. (2021). False discovery rates to detect signals from incomplete spatially aggregated data. Journal of Computational and Graphical Statistics, 30, 1081-1094 (doi:10.1080/10618600.2021.1873144).

Lucchesi, L.R., Kuhnert, P.M., and Wikle, C.K. (2021). Vizumap: an R package for visualising uncertainty in spatial data. Journal of Open Source Software, 6, 2409 (doi:10.21105/joss.02409).

North, J.S., Schliep, E.M., and Wikle, C.K. (2021) On the spatial and temporal shift in the archetypal seasonal temperature cycle as driven by annual and semi-annual harmonics, Environmetrics, 32, e2665 (doi:10.1002/env.266).

Raim, A.R., Holan, S.H., Bradley, J.R., and Wikle, C.K. (2021). An R package for spatio-temporal change of support. Computational Statistics, 36, 749–780 (doi:10.1007/s00180-020-01029-4).

Zammit-Mangion, A. and Cressie, N. (2021). FRK: An R package for spatial and spatio-temporal prediction with large datasets. Journal of Statistical Software, 98(4), 1-42 (doi:10.18637/jss.v098.i04).

2020

Bradley, J.R., Holan, S.H., and Wikle, C.K. (2020). Bayesian hierarchical models with conjugate full-conditional distributions for dependent data from the natural exponential family. Journal of the American Statistical Association, 115, 2037–2052 (doi:10.1080/01621459.2019.1677471).

Bradley, J.R., Wikle, C.K., and Holan, S.H. (2020). Hierarchical models for spatial data with errors that are correlated with the latent process. Statistica Sinica, 30, 81-109 (doi:10.5705/SS.202016.0230).

Cressie, N. and Suesse, T. (2020). Great expectations and even greater exceedances from spatially referenced data. Spatial Statistics, 37, 100420 (doi:10.1016/j.spasta.2020.100420).

Cressie, N. and Wikle, C.K. (2020). Measuring, mapping, and uncertainty quantification in the space-time cube. Revista Matemática Complutense, 33, 643-660 (doi:10.1007/s13163-020-00359-7).

Hooten, M.B., Wikle, C.K., and Schwob, M.R. (2020). Statistical implementations of agent-based demographic models. International Statistical Review, 88, 441–461 (doi:10.1111/insr.12399).

Katzfuss, M., Stroud, J.R., and Wikle, C.K. (2020). Ensemble Kalman methods for high-dimensional hierarchical dynamic space-time models. Journal of the American Statistical Association, 115, 866-885 (doi:10.1080/01621459.2019.1592753).

Stough, T., Cressie, N., Kang, E. L., Michalak, A. M., and Sahr, K. (2020). Spatial analysis and visualization of global data on multi-resolution hexagonal grids. Japanese Journal of Statistics and Data Science, 3, 107-128 (doi:10.1007/s42081-020-00077-w).

Zammit-Mangion, A. and Rougier, J. (2020). Multi-scale process modelling and distributed computation for spatial data. Statistics and Computing, 30, 1609-1627 (doi:10.1007/s11222-020-09962-6).

Zammit-Mangion, A. and Wikle, C.K. (2020). Deep integro-difference equation models for spatio-temporal forecasting. Spatial Statistics, 37, 100408 (doi:10.1016/j.spasta.2020.100408).

Zhang, B. and Cressie, N. (2020). Bayesian inference of spatio-temporal changes of Arctic sea ice. Bayesian Analysis, 15, 605-631 (doi:10.1214/20-BA1209).

2019

Bradley, J.R., Wikle, C.K., and Holan, S.H. (2019). Spatio-temporal models for big multinomial data using the conditional multivariate logit-beta distribution. Journal of Time Series Analysis, 40, 363-382 (doi:10.1111/jtsa.12468).

Cressie, N. and Hardouin, C. (2019). A diagonally weighted matrix norm between two covariance matrices. Spatial Statistics, 29, 316-328 (doi:10.1016/j.spasta.2019.01.001).

Gopalan, G., Hrafnkelsson, B., Wikle, C.K., Rue, H., Aðalgeirsdóttir, G., Jarosch, A., and Pálsson, F. (2019). A hierarchical spatiotemporal statistical model motivated by glaciology. Journal of Agricultural, Biological and Environmental Statistics, 24, 669-692 (doi:10.1007/s13253-019-00367-1).

Heaton, M. J., Datta, A., Finley, A. O., Furrer, R., Guinness, J., Guhaniyogi, R., Gerber, F., Gramacy, R. B., Hammerling, D., Katzfuss, M., Lindgren, F., Nychka, D. W., Sun, F., and Zammit-Mangion, A. (2019). A case study competition among methods for analyzing large spatial data. Journal of Agricultural, Biological and Environmental Statistics, 24, 398–425 (doi:10.1007/s13253-018-00348-w).

McDermott, P.L. and Wikle, C.K. (2019). Bayesian recurrent neural network models for forecasting and quantifying uncertainty in spatio-temporal data. Entropy, 21, 184 (doi:10.3390/e21020184).

McDermott, P.L. and Wikle, C.K. (2019). Deep echo state networks with uncertainty quantification for spatio-temporal forecasting. Environmetrics, 30, e2553 (doi:10.1002/env.2553).

Wikle, C.K. (2019). Comparison of deep neural networks and deep hierarchical models for spatio-temporal data. Journal of Agricultural, Biological and Environmental Statistics, 24, 175–203 (doi:10.1007/s13253-019-00361-7).