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The statistical properties of potential estimators of forest area for the USDA Forest Service's Forest Inventory and Analysis (FIA) program are presented and discussed. The current FIA area estimator is compared and contrasted with a weighted mean estimator and an estimator based on
the Polya posterior, in the presence of nonresponse. Estimator optimality is evaluated both theoretically and via simulation under bias and mean squared error criteria. The results indicate that, under realistic conditions, the current FIA area estimator can sometimes result in substantial
bias and have a higher mean squared error than both of the alternative estimators. This finding is of special interest because the same factor that contributes to this increased bias and variance applies to all area-based FIA estimates. The weighted mean and Polya posterior estimators gave
similar results for estimating the total area of a domain. It is concluded that the main advantage of the latter approach is that many other statistics are obtainable because the entire population distribution is estimated from the same sampling effort. The cost of this advantage for the Polya
posterior approach is that a single result requires many more computer operations, a cost that has become virtually ignorable over the past decade.
Forest Science is a peer-reviewed journal publishing fundamental and applied research that explores all aspects of natural and social sciences as they apply to the function and management of the forested ecosystems of the world. Topics include silviculture, forest management, biometrics, economics, entomology & pathology, fire & fuels management, forest ecology, genetics & tree improvement, geospatial technologies, harvesting & utilization, landscape ecology, operations research, forest policy, physiology, recreation, social sciences, soils & hydrology, and wildlife management.