- Volume 24, Number 2 (2018), 1531-1575.
Asymptotic analysis of covariance parameter estimation for Gaussian processes in the misspecified case
In parametric estimation of covariance function of Gaussian processes, it is often the case that the true covariance function does not belong to the parametric set used for estimation. This situation is called the misspecified case. In this case, it has been shown that, for irregular spatial sampling of observation points, Cross Validation can yield smaller prediction errors than Maximum Likelihood. Motivated by this observation, we provide a general asymptotic analysis of the misspecified case, for independent and uniformly distributed observation points. We prove that the Maximum Likelihood estimator asymptotically minimizes a Kullback–Leibler divergence, within the misspecified parametric set, while Cross Validation asymptotically minimizes the integrated square prediction error. In Monte Carlo simulations, we show that the covariance parameters estimated by Maximum Likelihood and Cross Validation, and the corresponding Kullback–Leibler divergences and integrated square prediction errors, can be strongly contrasting. On a more technical level, we provide new increasing-domain asymptotic results for independent and uniformly distributed observation points.
Bernoulli, Volume 24, Number 2 (2018), 1531-1575.
Received: November 2015
Revised: June 2016
First available in Project Euclid: 21 September 2017
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Bachoc, François. Asymptotic analysis of covariance parameter estimation for Gaussian processes in the misspecified case. Bernoulli 24 (2018), no. 2, 1531--1575. doi:10.3150/16-BEJ906. https://projecteuclid.org/euclid.bj/1505980903
- Figures and proof of the technical results. In the supplementary material , we provide Figures 1 and 2, complementing the one-dimensional illustrative Monte Carlo simulation. We also give the proof of the lemmas stated in Section A.6.