logLik.km: log-likelihood of a km object
In DiceKriging: Kriging Methods for Computer Experiments

Description Usage Arguments Value Author(s) References See Also

Returns the log-likelihood value of a km object.

1 2	## S4 method for signature 'km' logLik(object, ...)

`object`	an object of class `km` containing the trend and covariance structures.
`...`	no other argument for this method.

The log likelihood value.

O. Roustant, D. Ginsbourger, Ecole des Mines de St-Etienne

N.A.C. Cressie (1993), Statistics for spatial data, Wiley series in probability and mathematical statistics.

D. Ginsbourger, D. Dupuy, A. Badea, O. Roustant, and L. Carraro (2009), A note on the choice and the estimation of kriging models for the analysis of deterministic computer experiments, Applied Stochastic Models for Business and Industry, 25 no. 2, 115-131.

R. Li and A. Sudjianto (2005), Analysis of Computer Experiments Using Penalized Likelihood in Gaussian Kriging Models, Technometrics, 47 no. 2, 111-120.

K.V. Mardia and R.J. Marshall (1984), Maximum likelihood estimation of models for residual covariance in spatial regression, Biometrika, 71, 135-146.

J.D. Martin and T.W. Simpson (2005), Use of kriging models to approximate deterministic computer models, AIAA Journal, 43 no. 4, 853-863.

J.-S. Park and J. Baek (2001), Efficient computation of maximum likelihood estimators in a spatial linear model with power exponential covariogram, Computer Geosciences, 27 no. 1, 1-7.

C.E. Rasmussen and C.K.I. Williams (2006), Gaussian Processes for Machine Learning, the MIT Press, http://www.gaussianprocess.org/gpml/

J. Sacks, W.J. Welch, T.J. Mitchell, and H.P. Wynn (1989), Design and analysis of computer experiments, Statistical Science, 4, 409-435.

M.L. Stein (1999), Interpolation of spatial data, some theory for kriging, Springer.

km, logLikFun

DiceKriging documentation built on Feb. 24, 2021, 1:07 a.m.