Statistical Modelling 2 (2002), 265
Spatial moving average models are formed by convolving a simple, underlying process with a smoothing kernel. This simple construction device can lead to some very interesting spatial processes and appealing computational approaches for estimation. Clearly, the use of such moving average constructions to create spatial processes is hardly new – the idea of smoothing out a spatial Poisson process is mentioned in Matérn (1960). However recent advances in computing and renewed focus on challenging applications has brought new life to the spatial moving average. This workshop was a chance to see the state of the art in such models.
The workshop was hosted and supported by the National Research Center for Statistics and the Environment and took place May 20–22, 2001 at University of Washington in Seattle. The format consisted of ten hour long talks, followed by a half hour of lively floor discussion. The workshop participants included: Ron Barry, Julian Besa, Nicky Best, Noel Cressie, Monserrat Fuentes, Peter Guttorp, Mark Hancock, Dave Higdon, Katja Ickstadt, Konstantin Krivoruchko, Doug Nychka, Paul Sampson, Michael Stein, Jean Thiebeaux, Jay Ver Hoef, Chris Wikle and Robert Wolpert.
A number of innovative developments – both theoretical and applied – were presented and discussed at the workshop. Some of these developments are contained in the following four papers. Enjoy!
David Higdon
Los Alamos National Laboratoty
Guest Associate Editor