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Toward a diagnostic toolkit for linear models with Gaussian-process distributed random effects
[article]
2018
arXiv
pre-print
Gaussian processes (GPs) are widely used as distributions of random effects in linear mixed models, which are fit using the restricted likelihood or the closely-related Bayesian analysis. This article addresses two problems. First, we propose tools for understanding how data determine estimates in these models, using a spectral basis approximation to the GP under which the restricted likelihood is formally identical to the likelihood for a gamma-errors GLM with identity link. Second, to examine
arXiv:1805.01010v1
fatcat:75og37e2yfdddgafh4hqxdwwp4