Estimation of the Scale Parameter for a Misspecified Gaussian Process Model

Abstract: Parameters of the covariance kernel of a Gaussian process model often need to be estimated from the data generated by an unknown Gaussian process. We consider fixed-domain asymptotics of the maximum likelihood estimator of the scale parameter under smoothness misspecification. If the covariance kernel of the data-generating process has smoothness $\nu_0$ but that of the model has smoothness $\nu \geq \nu_0$, we prove that the expectation of the maximum likelihood estimator is of the order $N^{2(\nu-\nu_0)/d}$ if the $N$ observation points are quasi-uniform in $[0, 1]^d$. This indicates that maximum likelihood estimation of the scale parameter alone is sufficient to guarantee the correct rate of decay of the conditional variance. We also discuss a connection the expected maximum likelihood estimator has to Driscoll's theorem on sample path properties of Gaussian processes. The proofs are based on reproducing kernel Hilbert space techniques and worst-case case rates for approximation in Sobolev spaces.