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Statistical Models and Estimators of the Proportion of Volume by Log Position

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A conditioned regression model for the proportion of volume by log position was proposed previously. In it, estimates of the conditioned regression coefficients were calculated by a step-by-step application of least squares. This paper presents the statistical theory for the earlier procedures. The estimators are shown to be linear combinations of multivariate least squares estimators of the average proportion of volume in each log position. Expressions for the covariance matrix of the estimator are derived. Simultaneous estimators of the conditioned regression coefficients are derived by multiple regression methods. The two methods of estimating the regression coefficients are compared, using volume measurements on peeler logs from 680 loblolly pines. Forest Sci. 20:357-362.

Keywords: Conditioned regression; least squares; mathematical models; multivariate least squares

Document Type: Journal Article

Affiliations: Biological Statistician, USDA Forest Service Northeastern Forest Experiment Station, Upper Darby, Pennsylvania

Publication date: December 1, 1974

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.
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