Applications of Ridge Regression in Forestry
Describes the use of ridge regression for dealing with multicollinearity in multiple linear regression. Ridge regression is reviewed and three criteria for selecting the "best" ridge estimator--ridge trace, variance inflation factor, and determinant of the correlation matrix--are discussed. The first application demonstrates the use of ridge regression for selecting independent variables during the development of a ponderosa pine basal area growth model. This use of ridge regression produced a meaningful predictive model with interpretable coefficients. The second application uses ridge regression to develop a descriptive model for estimating bare land values in the Douglas-fir region. The objective was to produce precise and stable estimates of model parameters and not to predict the dependent variable. The resulting bare land value estimates fall in the range of values produced by other techniques. Forest Sci. 27:339-348.
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Document Type: Journal Article
Affiliations: Assistant Professor, School of Forestry, Oregon State University, Corvallis
Publication date: 1981-06-01
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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.
Forest Science is published bimonthly in February, April, June, August, October, and December.
2016 Impact Factor: 1.782 (Rank 17/64 in forestry)
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June 1, 2016 to Feb. 28, 2017
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