Fixed-domain asymptotics for a subclass of Matern-type Gaussian random fields
By Wei-Liem Loh. 2005
71 citations recorded by the source corpus.
Stein [Statist Sci. 4 (1989) 432-433] proposed the Matern-type Gaussian random fields as a very flexible class of models for computer experiments. This article considers a subclass of these models that are exactly once mean square differentiable. In particular, the likelihood function is determined in closed form. and under mild conditions the sieve maximum likelihood estimators for the parameters of the covariance function are shown to be weakly consistent with respect to fixed-domain asymptotics.
Subfields: Environmental Engineering, Management Science and Operations Research, Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/a0ab040ab9bb481f75c93654b6e75cc1c4f2b5bd
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In Environmental Engineering
In Management Science and Operations Research
In Statistics and Probability
Citations in the corpus
- On the consistent separation of scale and variance for Gaussian random fields (2009, background): “In related work, Loh [23] shows that maximum likelihood estimates of scale and variance parameters in a non-isotropic multiplicative Matérn model are consistent when ν = 3/2 (similar results for the Gaussian autocovariance model can be found in [24]).”