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Parameter Estimation of Base–Age Invariant Site Index Models: Which Data Structure to Use?—A Discussion

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We discuss here methods of comparing various approaches to site-dependent height–age model-parameter estimation. The discussion emphasis is on proper analysis of the varying parameter (VP) method versus several variants of the algebraic difference methods including one with the error in variable (EIV) theory adaptation. The study has demonstrated that, when properly considered, the VP method consistently outperforms all the other methods including the EIV method. The EIV method was tested on data with known “true model” and drastically simplified errors, and had a root mean square error (RMSE) more than 10 times worse than the VP method. All the other methods produced drastically and uniquely differing results with RMSEs varying from 26 times greater than the VP method to 300 times greater. The patterns of relatively close similarity between the EIV and the VP methods and the large differing among the other methods was similar for fittings to real data of second-rotation loblolly pine (Pinus taeda L.) from a designed experiment. The VP method is recommended for use over all the other methods as the most unbiased and as much simpler than the EIV method, which was second best.

Keywords: base–age invariant; difference method; dummy variable; errors in variables; parameter estimation; site index model; varying parameter

Document Type: Research Article

Publication date: October 1, 2007

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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.

    2015 Impact Factor: 1.702
    Ranking: 16 of 66 in forestry

    Also published by SAF:
    Journal of Forestry
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