Some covariance models based on normal scale mixtures
Copyright policy )Some covariance models based on normal scale mixtures
Modelling spatio-temporal processes has become an important issue in current research. Since Gaussian processes are essentially determined by their second order structure, broad classes of covariance functions are of interest. Here, a new class is described that merges and generalizes various models presented in the literature, in particular models in Gneiting (J. Amer. Statist. Assoc. 97 (2002) 590--600) and Stein (Nonstationary spatial covariance functions (2005) Univ. Chicago). Furthermore, new models and a multivariate extension are introduced.
Published in at http://dx.doi.org/10.3150/09-BEJ226 the Bernoulli (http://isi.cbs.nl/bernoulli/) by the International Statistical Institute/Bernoulli Society (http://isi.cbs.nl/BS/bshome.htm)
- University of Göttingen Germany
- University of Mannheim Germany
Inference from spatial processes, Random fields; image analysis, Gneiting's class, Mathematics - Statistics Theory, Statistics Theory (math.ST), Gneiting’s class, 510, FOS: Mathematics, Applications of statistics to environmental and related topics, rainfall model, cross covariance function, spatio-temporal model
Inference from spatial processes, Random fields; image analysis, Gneiting's class, Mathematics - Statistics Theory, Statistics Theory (math.ST), Gneiting’s class, 510, FOS: Mathematics, Applications of statistics to environmental and related topics, rainfall model, cross covariance function, spatio-temporal model
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