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The Two-Stage Method for Measurement Error Characterization

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A measurement error (ME) is a component of any study involving the use of actual measurements, but is often not recognized or is ignored. The consequences of ME on models can be severe, affecting estimates of tree and stand attributes and model parameters. Although correction methods do exist for countering the effects of ME, the use of these methods requires knowledge of the distribution of the errors. A new method for modeling error distributions, called the two-stage error distribution (TSED) method, is presented here. This method is compared with traditional methods for error modeling through an example using diameter and height ME. Comparisons between the fitted error distribution surfaces and the empirical error surface are based on a dissimilarity measure. The results indicate that the TSED method produces a much more accurate and precise characterization of the ME distribution than do traditional methods when a high percentage of errors is identical. In other cases, the TSED method works as well as the most accurate form of the traditional method. The TSED method is also expected to perform better at characterizing asymmetric distributions. It is therefore more adaptable than traditional methods and is being proposed for error modeling in the future. FOR. SCI. 50(6):743–756.
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Keywords: Measurement error; environmental management; forest; forest management; forest resources; forestry; forestry research; forestry science; modeling; multinomial regression; natural resource management; natural resources; nonlinear regression; statistical distribution

Document Type: Regular Article

Affiliations: 1: Biometrician FORSight Resources, LLC Park Tower One, 201 SE Park Plaza Dr., Suite 283 Vancouver WA 98684 Phone: 360-260-3281;, Fax: 360-254-1908, Email: [email protected] 2: Department of Forest Resources Oregon State University Corvallis OR 97331-5752 Phone: 541-737-2687;, Fax: 541-737-3049, Email: [email protected]

Publication date: 2004-12-01

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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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    June 1, 2016 to Feb. 28, 2017

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