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Prediction Bias and Response Surface Curvature

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Many functions used for forestry applications have response surfaces that have curvature. Depending on the degree of curvature, predictions made with these functions can be biased when the inputs to these functions have unbiased random errors. An approximation is presented for calculating the bias in prediction when there are unbiased but random errors in the inputs of the function. Examples of some fairly simple functions used in forestry are shown to provide highly biased predictions in typical applications. For. Sci. 37(3):755-765.
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Keywords: Errors in predictors

Document Type: Journal Article

Affiliations: Associate Professor of Forest Biometrics, 110 Mumford Hall, Department of Forestry, 1301 W. Gregory Drive, University of Illinois, Urbana, IL 61801

Publication date: 1991-08-01

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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)

    Average time from submission to first decision: 62.5 days*
    June 1, 2016 to Feb. 28, 2017

    Also published by SAF:
    Journal of Forestry
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