ConserveOnline - Optimal Sampling Design for Estimation and Prediction on Stream Networks, by Dale Zimmerman

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Optimal Sampling Design for Estimation and Prediction on Stream Networks, by Dale Zimmerman

By Web Admin on 6/5/2007 | Keyword(s): Session v: statistical approaches and field sampling design

The quality of statistical inferences for regression models on stream networks is affected substantially by the spatial configuration of the sites where measurements are taken. Moreover, a sampling design that yields high-quality inferences of one kind, e.g. estimation of regression parameters, may yield sub-par inferences of another kind, e.g. spatial prediction. In this talk, I propose design criteria corresponding to several kinds of inferences, and I obtain and compare optimal designs with respect to each criterion for a relatively small (6-point) design on a stream network of order 3. The effects that the assumed mean function, the spatial covariance function (flow-only versus flow-and-distance models), and the strength of spatial correlation have on optimal design are investigated. It is hoped that these comparisons and investigations will provide some insight into good general sampling principles on stream networks.

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