Kriging Coverage before Online Covariates Arrive
Abstract
Offline application and stochastic kriging share the problem of preparing evidence for contexts observed later. Validation should freeze the fitted relationship and hold out meaningful regions. Direct local checks can distinguish useful borrowing from apparent precision caused by a wrong covariance assumption. Application data arriving afterward define a later update policy rather than prior evidence. This focused synthesis develops an empirical comparison that preserves the terminal action, observation process, and total verification cost. Its proposed tests are prospective; no new simulation run, model-training result, or intervention effect is reported.
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