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An Estimator of Prediction Error Variance for Projection Equations

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Abstract:

An estimator of prediction error variance for projection equations was derived using the first-order Taylor expansion in this study. The estimator, a modified estimator of the prediction error variance for a population mean regression model, was adapted for situations in which projection equations are applied to unsampled individuals. The estimator accounted for the errors associated with the response variable on the right side of a projection equation, as well as the errors associated with parameter estimation and serial correlations in data. The application of the estimator was demonstrated using the example of 140 trees with diameters measured annually from age 5 to 19. The results indicated that the estimator represented prediction error variance well and was useful for constructing prediction intervals for a given projection equation and providing information on the contributions of the variance components associated with different error sources. It was evident that the error associated with the prior observation of the response variable of a projection equation had an important impact on model predictions for forward projections. Overlooking this variance component may result in significant misestimation for the prediction errors of the projection equation.

Keywords: Schumacher function; dummy variable regression; forest growth and yield; generalized nonlinear least squares; variance components of prediction

Document Type: Research Article

Publication date: 2008-10-01

More about this publication?
  • 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

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

    Also published by SAF:
    Journal of Forestry
    Other SAF Publications
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