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Suppose that a random process Z(s; t), indexed in space and time, has a spatio-temporal stationary covariance C(h; u), where h 2 IR d (d 1) is a spatial lag and u 2 IR is a temporal lag. Separable spatio-temporal covariances have the property that they can be written as a product of a purely spatial covariance and a purely temporal covariance. Their ease of(More)
Polar orbiting satellites remotely sense the earth and its atmosphere, producing data sets that give daily global coverage. For any given day, the data are many and spatially irregular. Our goal in this article is to predict values that are spatially regular at diierent resolutions; such values are often used as input to general circulation models (GCMs)(More)
Recently, a class of multiscale tree-structured models was introduced in terms of scale-recursive dynamics deened on trees. The main advantage of these models is their association with a fast, recursive, Kalman-lter prediction algorithm. In this article, we propose a more general class of multiscale graphical models over acyclic directed graphs, for use in(More)
Nonparametric hypothesis testing for a spatial signal can involve a large numberofhypotheses. For instance, two satellite images of the same scene, taken before and after an event, could be used to test a hypothesis that the event h a s no environmental impact. This is equivalent to testing that the mean diierence of \after;before" is zero at each of the(More)
An autologistic regression model consists of a linear regression of a response variable on explanatory variables and an auto-regression on responses at neighboring locations on a lattice. It is a Markov random eld with pairwise spatial dependence and is a popular tool for modeling spatial binary responses. In this article, we add a temporal component to the(More)
In this article, we propose a regression method for simultaneous supervised clustering and feature selection over a given undirected graph, where homogeneous groups or clusters are estimated as well as informative predictors, with each predictor corresponding to one node in the graph and a connecting path indicating a priori possible grouping among the(More)
—Due to its effectiveness for removing heavy-tail noise and preserving abrupt structures hidden in noisy data, median filtering has long been a popular tool for signal restoration. In practice , an important issue of applying median filtering is the choice of the span. In this letter, we develop a data adaptive criterion for choosing this span. This(More)