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Two approaches toward fitting regression models with multiplicative error heteroscedasticity recur in the forestry, ecology, and statistical literature. One includes the estimation of the heterogeneity in the fitting process. The alternative approach entails the use of variance estimators that are robust to the error variance heterogeneity. Under suitable conditions, the former method offers nonnegligible gains in efficiency, whereas the robust alternatives provide accurate assessment of ordinary least squares estimators even in the presence of heteroscedasticity. The performance of both approaches are examined and contrasted, and suggestions for future applications and research are made on the basis of these results. For. Sci. 35(1):105-125.
Research Scientist with Westvaco Corporation, Summerville, SC 29484
Publication date: March 1, 1989
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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.