Prediction of Gross Tree Volume Using Regression Models with Non-Normal Error Distributions
Abstract:Previous work in weighted linear regression, where weight functions are used to obtain homogeneous variance on a transformed scale, has often assumed that the errors are normally distributed. In a study of four data sets, three of which were actual data sets with unknown error distributions and one an artificial set with a known error distribution, this assumption is incorrect. Consequently, we tested a transformation of the normal distribution, called the SU distribution, and compared it with the normal as an alternative. For three of the four data sets studied, the SU distribution was superior. Prediction intervals and biases for the regression estimators generated using the SU and normal distributions were also evaluated. Results for the SU distribution bettered those for the normal distribution in three of the four data sets. For the remaining data set, they were comparable. For. Sci. 42(4):419-430.
Document Type: Journal Article
Affiliations: Multiresource Inventory Techniques, Rocky Mountain Forest and Range Experiment Station, USDA Forest Service, 240 W. Prospect, Fort Collins, Colorado 80526-2098<
Publication date: 1996-11-01
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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.
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