Resumé
| Originalsprog | Engelsk |
|---|---|
| Tidsskrift | Statistics & Probability Letters |
| Vol/bind | 81 |
| Udgave nummer | 7 |
| Sider (fra-til) | 749-758 |
| Antal sider | 10 |
| ISSN | 0167-7152 |
| DOI | |
| Status | Udgivet - 2011 |
Emneord
- population
- bentisk fauna
Citer dette
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Bias-corrected Pearson estimating functions for Taylor’s power law applied to benthic macrofauna data. / Jørgensen, Bent; Demétrio, Clarice G.B.; Kristensen, Erik; Banta, Gary Thomas; Petersen, Hans Christian; Delfosse, Matthieu.
I: Statistics & Probability Letters, Bind 81, Nr. 7, 2011, s. 749-758.Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › peer review
TY - JOUR
T1 - Bias-corrected Pearson estimating functions for Taylor’s power law applied to benthic macrofauna data
AU - Jørgensen, Bent
AU - Demétrio, Clarice G.B.
AU - Kristensen, Erik
AU - Banta, Gary Thomas
AU - Petersen, Hans Christian
AU - Delfosse, Matthieu
PY - 2011
Y1 - 2011
N2 - 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.
AB - 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.
KW - population
KW - bentisk fauna
U2 - 10.1016/j.spl.2011.01.005
DO - 10.1016/j.spl.2011.01.005
M3 - Journal article
VL - 81
SP - 749
EP - 758
JO - Statistics & Probability Letters
JF - Statistics & Probability Letters
SN - 0167-7152
IS - 7
ER -