Gaussian f -regular processes and the asymptotic behavior of the likelihood function

1984 ◽  
Vol 27 (5) ◽  
pp. 3140-3151
Author(s):  
V. N. Solev

2010 ◽  
Vol 2010 ◽  
pp. 1-17 ◽  
Author(s):  
Ralf Zimmermann

The covariance structure of spatial Gaussian predictors (aka Kriging predictors) is generally modeled by parameterized covariance functions; the associated hyperparameters in turn are estimated via the method of maximum likelihood. In this work, the asymptotic behavior of the maximum likelihood of spatial Gaussian predictor models as a function of its hyperparameters is investigated theoretically. Asymptotic sandwich bounds for the maximum likelihood function in terms of the condition number of the associated covariance matrix are established. As a consequence, the main result is obtained:optimally trained nondegenerate spatial Gaussian processes cannot feature arbitrary ill-conditioned correlation matrices. The implication of this theorem on Kriging hyperparameter optimization is exposed. A nonartificial example is presented, where maximum likelihood-based Kriging model training is necessarily bound to fail.



Author(s):  
Antara Dasgupta ◽  
Renaud Hostache ◽  
RAAJ Ramasankaran ◽  
Guy J.‐P Schumann ◽  
Stefania Grimaldi ◽  
...  


1986 ◽  
Vol 149 (8) ◽  
pp. 709 ◽  
Author(s):  
I.I. Abbasov ◽  
Boris M. Bolotovskii ◽  
Valerii A. Davydov




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