TY - JOUR T1 - Bayesian Hierarchical Statistical SIRS Models JF - Statistical Methods and Applications Y1 - 2015 A1 - Zhuang, L. A1 - Cressie, N. VL - 23 ER - TY - JOUR T1 - Capturing multivariate spatial dependence: Model, estimate, and then predict JF - Statistical Science Y1 - 2015 A1 - Cressie, N. A1 - Burden, S. A1 - Davis, W. A1 - Krivitsky, P. A1 - Mokhtarian, P. A1 - Seusse, T. A1 - Zammit-Mangion, A. VL - 30 UR - http://projecteuclid.org/euclid.ss/1433341474 IS - 2 ER - TY - JOUR T1 - Comment on Article by Ferreira and Gamerman JF - Bayesian Analysis Y1 - 2015 A1 - Cressie, N. A1 - Chambers, R. L. VL - 10 UR - http://projecteuclid.org/euclid.ba/1429880217 IS - 3 ER - TY - JOUR T1 - Comment: Spatial sampling designs depend as much on “how much?” and “why?” as on “where?” JF - Bayesian Analysis Y1 - 2015 A1 - Cressie, N. A1 - Chambers, R. L. AB - A comment on “Optimal design in geostatistics under preferential sampling” by G. da Silva Ferreira and D. Gamerman ER - TY - JOUR T1 - Comparing and selecting spatial predictors using local criteria JF - Test Y1 - 2015 A1 - Bradley, J.R. A1 - Cressie, N. A1 - Shi, T. VL - 24 UR - http://dx.doi.org/10.1007/s11749-014-0415-1 IS - 1 ER - TY - CHAP T1 - Evaluation of diagnostics for hierarchical spatial statistical models T2 - Geometry Driven Statistics Y1 - 2015 A1 - Cressie, N. A1 - Burden, S. ED - I.L. Dryden ED - J.T. Kent JF - Geometry Driven Statistics PB - Wiley CY - Chinchester SN - 978-1118866573 UR - http://niasra.uow.edu.au/content/groups/public/@web/@inf/@math/documents/doc/uow169240.pdf ER - TY - JOUR T1 - Figures of merit for simultaneous inference and comparisons in simulation experiments JF - Stat Y1 - 2015 A1 - Cressie, N. A1 - Burden, S. VL - 4 UR - http://onlinelibrary.wiley.com/doi/10.1002/sta4.88/epdf IS - 1 ER - TY - JOUR T1 - Hot enough for you? A spatial exploratory and inferential analysis of North American climate-change projections JF - Mathematical Geosciences Y1 - 2015 A1 - Cressie, N. A1 - Kang, E.L. UR - http://dx.doi.org/10.1007/s11004-015-9607-9 ER - TY - JOUR T1 - Multivariate Spatial Covariance Models: A Conditional Approach Y1 - 2015 A1 - Cressie, N. A1 - Zammit-Mangion, A. AB - Multivariate geostatistics is based on modelling all covariances between all possible combinations of two or more variables at any sets of locations in a continuously indexed domain. Multivariate spatial covariance models need to be built with care, since any covariance matrix that is derived from such a model must be nonnegative-definite. In this article, we develop a conditional approach for spatial-model construction whose validity conditions are easy to check. We start with bivariate spatial covariance models and go on to demonstrate the approach's connection to multivariate models defined by networks of spatial variables. In some circumstances, such as modelling respiratory illness conditional on air pollution, the direction of conditional dependence is clear. When it is not, the two directional models can be compared. More generally, the graph structure of the network reduces the number of possible models to compare. Model selection then amounts to finding possible causative links in the network. We demonstrate our conditional approach on surface temperature and pressure data, where the role of the two variables is seen to be asymmetric. UR - https://arxiv.org/abs/1504.01865 ER - TY - JOUR T1 - Rejoinder on: Comparing and selecting spatial predictors using local criteria JF - Test Y1 - 2015 A1 - Bradley, J.R. A1 - Cressie, N. A1 - Shi, T. VL - 24 UR - http://dx.doi.org/10.1007/s11749-014-0414-2 IS - 1 ER - TY - JOUR T1 - The SAR model for very large datasets: A reduced-rank approach JF - Econometrics Y1 - 2015 A1 - Burden, S. A1 - Cressie, N. A1 - Steel, D.G. VL - 3 UR - http://www.mdpi.com/2225-1146/3/2/317 IS - 2 ER - TY - JOUR T1 - A Comparison of Spatial Predictors when Datasets Could be Very Large JF - ArXiv Y1 - 2014 A1 - Bradley, J. R. A1 - Cressie, N. A1 - Shi, T. KW - Statistics - Methodology AB -

In this article, we review and compare a number of methods of spatial prediction. To demonstrate the breadth of available choices, we consider both traditional and more-recently-introduced spatial predictors. Specifically, in our exposition we review: traditional stationary kriging, smoothing splines, negative-exponential distance-weighting, Fixed Rank Kriging, modified predictive processes, a stochastic partial differential equation approach, and lattice kriging. This comparison is meant to provide a service to practitioners wishing to decide between spatial predictors. Hence, we provide technical material for the unfamiliar, which includes the definition and motivation for each (deterministic and stochastic) spatial predictor. We use a benchmark dataset of CO2 data from NASA's AIRS instrument to address computational efficiencies that include CPU time and memory usage. Furthermore, the predictive performance of each spatial predictor is assessed empirically using a hold-out subset of the AIRS data.

UR - http://arxiv.org/abs/1410.7748 IS - 1410.7748 ER - TY - JOUR T1 - Spatial Fay-Herriot Models for Small Area Estimation with Functional Covariates JF - Spatial Statistics Y1 - 2014 A1 - Porter, A. T., A1 - Holan, S.H., A1 - Wikle, C.K., A1 - Cressie, N. VL - 10 UR - http://arxiv.org/pdf/1303.6668v3.pdf ER - TY - ABST T1 - Bayesian inference for the Spatial Random Effects Model Y1 - 2013 A1 - Cressie, N. JF - Department of Statistics, Macquarie University PB - Macquarie University ER - TY - CONF T1 - Comparing and Selecting Predictors Predictors Using Local Criteria T2 - International Workshop on Recent Advances in Statistical Inference: Theory and Case Studies Y1 - 2013 A1 - Cressie, N. JF - International Workshop on Recent Advances in Statistical Inference: Theory and Case Studies PB - International Workshop on Recent Advances in Statistical Inference: Theory and Case Studies CY - Padua, Italy ER - TY - JOUR T1 - Hierarchical Spatio-Temporal Models and Survey Research JF - Statistics Views Y1 - 2013 A1 - Wikle, C. A1 - Holan, S. A1 - Cressie, N. UR - http://www.statisticsviews.com/details/feature/4730991/Hierarchical-Spatio-Temporal-Models-and-Survey-Research.html ER - TY - JOUR T1 - Hierarchical Statistical Modeling of Big Spatial Datasets Using the Exponential Family of Distributions JF - Spatial Statistics Y1 - 2013 A1 - Sengupta, A. A1 - Cressie, N. KW - EM algorithm KW - Empirical Bayes KW - Geostatistical process KW - Maximum likelihood estimation KW - MCMC KW - SRE model VL - 4 UR - http://www.sciencedirect.com/science/article/pii/S2211675313000055 ER - TY - ABST T1 - How can survey estimates of small areas be improved by leveraging social-media data? Y1 - 2013 A1 - Cressie, N. A1 - Holan, S. A1 - Wikle, C. JF - The Survey Statistician UR - http://isi.cbs.nl/iass/N68.pdf ER - TY - ABST T1 - Some Historical Remarks on Spatial Statistics, Spatio-Temporal Statistics Y1 - 2013 A1 - Cressie, N. JF - Reading Group, University of Missouri ER - TY - ABST T1 - Statistics for Spatio-Temporal Data Y1 - 2013 A1 - Cressie, N. JF - Invited One-Day Short Course at the U.S. Census Bureau ER - TY - ABST T1 - Hierarchical Statistical Modeling of Big Spatial Datasets Using the Exponential Family of Distributions Y1 - 2012 A1 - Sengupta, A. A1 - Cressie, N. PB - The Ohio State University ER - TY - ABST T1 - Inference for Count Data using the Spatial Random Effects Model Y1 - 2012 A1 - Cressie, N. ER -