Local likelihood estimation for nonstationary random fields

• Authors: Ethan B. Anderes;Michael L. Stein
• Venue: Journal of Multivariate Analysis
• Year: 2011

We develop a weighted local likelihood estimate for the parameters that govern the local spatial dependency of a locally stationary random field. The advantage of this local likelihood estimate is that it smoothly downweights the influence of faraway observations, works for irregular sampling locations, and when designed appropriately, can trade bias and variance for reducing estimation error. This paper starts with an exposition of our technique on the problem of estimating an unknown positive function when multiplied by a stationary random field. This example gives concrete evidence of the benefits of our local likelihood as compared to unweighted local likelihoods. We then discuss the difficult problem of estimating a bandwidth parameter that controls the amount of influence from distant observations. Finally we present a simulation experiment for estimating the local smoothness of a local Matern random field when observing the field at random sampling locations in [0,1]^2. The local Matern is a fully nonstationary random field, has a closed form covariance, can attain any degree of differentiability or Holder smoothness and behaves locally like a stationary Matern. We include an appendix that proves the positive definiteness of this covariance function.