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Max-stable processes arise from an infinite-dimensional generalisation of extreme value theory. They form a natural class of processes when sample maxima are observed at each site of a spatial process, a problem of particular interest in connection with regional estimation methods in hy-drology. A general representation of max-stable processes due to de(More)
0 Abstract A Bayesian statistical model is proposed that combines information from a multi-model ensemble of atmosphere-ocean general circulation models and observations to determine probability distributions of future temperature change on a regional scale. The posterior distributions derived from the statistical assumptions incorporate the criteria of(More)
Spatial estimations are increasingly used to estimate geocoded ambient particulate matter (PM) concentrations in epidemiologic studies because measures of daily PM concentrations are unavailable in most U.S. locations. This study was conducted to a) assess the feasibility of large-scale kriging estimations of daily residential-level ambient PM(More)
Time-series studies that use daily mortality and ambient ozone concentrations exhibit estimates of ozone effects that are variable across cities. We investigate this intercity variability, as well as the sensitivity of the ozone- mortality associations to modeling assumptions and choice of daily ozone metric, based on reanalysis of data from the National(More)
Parameters of Gaussian multivariate models are often estimated using the maximum likelihood approach. In spite of its merits, this methodology is not practical when the sample size is very large, as, for example, in the case of massive geo-referenced data sets. In this paper, we study the asymptotic properties of the es-timators that minimize three(More)
[1] We propose a method of analyzing spatiotemporal data by decomposition into deterministic nonparametric functions of time and space, linear functions of other covariates, and a random component that is spatially, though not temporally, correlated. The resulting model is used for spatial interpolation and especially for estimation of a spatially dependent(More)
A second-order expansion is established for predictive distributions in Gaussian processes with estimated covariances. Particular focus is on estimating quantiles of the predictive distribution and their subsequent application to prediction intervals. Two basic approaches are considered, (a) a " plug-in " approach using the restricted maximum likelihood(More)
Projections of future climate change caused by increasing greenhouse gases depend critically on numerical climate models coupling the ocean and atmosphere (GCMs). However, different models differ substantially in their projections, which raises the question of how the different models can best be combined into a probability distribution of future climate(More)