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