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

Bent Jørgensen, Clarice G.B. Demétrio, Erik Kristensen, Gary T. Banta, Hans Christian Petersen and Matthieu Delefosse

Statistics & Probability Letters, 2011, vol. 81, issue 7, 749-758

Abstract: 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.

Keywords: Generalized; linear; model; Newton; scoring; algorithm; Power; variance; function; Species; abundance; data; Tweedie; distribution (search for similar items in EconPapers)
Date: 2011
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