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A ratio estimator for bias correction in logarithmic regressions

A ratio estimator for bias correction in logarithmic regressions <jats:p> It is recommended that the proportional bias in logarithmic regressions be estimated from the ratio of the arithmetic sample mean and the mean of the back-transformed predicted values from the regression. Under the assumption of a lognormal distribution of errors, the conditions for application of this ratio estimator are optimal. A simulated sampling study has shown that this method gives more reliable results than the methods recommended by Baskerville (G.L. Baskerville. 1972. Can. J. For. Res. 2: 49–53) or that derived by Finney (D.J. Finney. 1941. J. R. Stat. Soc. 7(Suppl): 55–61). The new method is also less sensitive to departures from the assumption of a lognormal distribution than the other two methods. </jats:p> http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Canadian Journal of Forest Research CrossRef

A ratio estimator for bias correction in logarithmic regressions

Canadian Journal of Forest Research , Volume 21 (5): 720-724 – May 1, 1991

A ratio estimator for bias correction in logarithmic regressions


Abstract

<jats:p> It is recommended that the proportional bias in logarithmic regressions be estimated from the ratio of the arithmetic sample mean and the mean of the back-transformed predicted values from the regression. Under the assumption of a lognormal distribution of errors, the conditions for application of this ratio estimator are optimal. A simulated sampling study has shown that this method gives more reliable results than the methods recommended by Baskerville (G.L. Baskerville. 1972. Can. J. For. Res. 2: 49–53) or that derived by Finney (D.J. Finney. 1941. J. R. Stat. Soc. 7(Suppl): 55–61). The new method is also less sensitive to departures from the assumption of a lognormal distribution than the other two methods. </jats:p>

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Publisher
CrossRef
ISSN
0045-5067
DOI
10.1139/x91-101
Publisher site
See Article on Publisher Site

Abstract

<jats:p> It is recommended that the proportional bias in logarithmic regressions be estimated from the ratio of the arithmetic sample mean and the mean of the back-transformed predicted values from the regression. Under the assumption of a lognormal distribution of errors, the conditions for application of this ratio estimator are optimal. A simulated sampling study has shown that this method gives more reliable results than the methods recommended by Baskerville (G.L. Baskerville. 1972. Can. J. For. Res. 2: 49–53) or that derived by Finney (D.J. Finney. 1941. J. R. Stat. Soc. 7(Suppl): 55–61). The new method is also less sensitive to departures from the assumption of a lognormal distribution than the other two methods. </jats:p>

Journal

Canadian Journal of Forest ResearchCrossRef

Published: May 1, 1991

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