Meteorological and environmental data that are collected at regular time intervals on a fixed monitoring network can be usefully studied combining ideas from multiple time series and spatial statistics, particularly when there are little or no missing data. This work investigates methods for...

Consider linear predictions of a stationary random field at an unobserved location in a bounded region as the observations become increasingly dense in that region. Suppose the ratio of the actual spectral density of the process to the spectral density used to generate the linear predictions...

Likelihood methods are often difficult to use with large, irregularly sited spatial data sets, owing to the computational burden. Even for Gaussian models, exact calculations of the likelihood for "n" observations require "O"("n"-super-3) operations. Since any joint density can be written as a...

We develop a weighted local likelihood estimate for the parameters that govern the local spatial dependency of a locally stationary random field. The advantage of this local likelihood estimate is that it smoothly downweights the influence of faraway observations, works for irregular sampling...