Statistica Sinica 28 (2018), 601-619
Abstract: Gaussian processes models are widely adopted for nonparameteric/semiparametric modeling. Identifiability issues occur when the mean model contains polynomials with unknown coefficients. Though resulting prediction is unaffected, this leads to poor estimation of the coefficients in the mean model, and thus the estimated mean model loses interpretability. This paper introduces a new Gaussian process model whose stochastic part is orthogonal to the mean part to address this issue. This paper also discusses applications to multi-fidelity simulations using data examples.
Key words and phrases: Computer experiments, identifiability, kriging, multifidelity simulations, universal kriging.