Optimal Predictive Inference in Log-Gaussian Random Fields

Victor De Oliveira
Department of Management Science and Statistics
The University of Texas at San Antonio

This talk reviews work on optimal predictive inference in log-Gaussian random fields. The two problems to be considered are:

(a) prediction of process values at unmeasured locations and
(b) prediction of process integrals over bounded regions.

Optimal predictors, within certain classes, are given for problems (a) and (b) as well as comparisons with other commonly used predictors. Shortest prediction intervals, within certain classes, are also given for problem (a). Finally, a brief discussion is given about some difficulties in computing prediction intervals for problem (b).