Unidentified prediction-risk rankings under spatial sampling with fixed inclusion probabilities
Constructs spatial sampling designs with equal inclusion probabilities that yield different prediction risk rankings.
Abstract
For Gaussian spatial prediction, we construct positive sampling mechanisms with identical observation laws and conditional site inclusion probabilities, yet opposite risk rankings for two fixed predictors. The construction selects two of four sites and allows independent Gaussian measurement error.