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Remarks on "Generalized Least Squares Estimation of Yield Functions" by I. S. Ferguson and J. W. Leech

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In building forest simulation models it is common to use data containing repeated measurements from individual sample plots. It is inappropriate to use ordinary least squares (OLS) regression to estimate the parameters of a model with such data, since this method will tend to underestimate the variances of the parameter estimates. Ferguson and Leech (1978) developed theory to use generalized least squares (GLS) regression to attempt to solve this problem. They used GLS to construct a stand-volume yield function for unthinned Pinus radiata D. Don in southeast South Australia. This paper corrects an error in their theory of GLS and shows the corresponding changes in their numerical results. There are theoretical and practical difficulties in applying the theory, and great care is needed with its use. Forest Sci. 27:233-239.

Keywords: Pinus radiata; Statistical methods; generalized least squares; mathematical models

Document Type: Journal Article

Affiliations: Research Scientist, Division of Forest Research, CSIRO, Stowell Ave., Hobart, Tas. 7000, Australia

Publication date: June 1, 1981

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