Bias-corrected Pearson estimating functions for Taylor’s power law applied to benthic macrofauna data

Bent Jørgensen, Clarice G.B. Demétrio, Erik Kristensen, Gary Thomas Banta, Hans Christian Petersen, Matthieu Delfosse

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Estimation of Taylor’s power law for species abundance data may be performed by linear regression of the log empirical variances on the log means, but this method suffers from a problem of bias for sparse data. We show that the bias may be reduced by using a bias-corrected Pearson estimating function. Furthermore, we investigate a more general regression model allowing for site-specific covariates. This method may be efficiently implemented using a Newton scoring algorithm, with standard errors calculated from the inverse Godambe information matrix. The method is applied to a set of biomass data for benthic macrofauna from two Danish estuaries.
Original languageEnglish
JournalStatistics & Probability Letters
Issue number7
Pages (from-to)749-758
Number of pages10
Publication statusPublished - 2011

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