Barry, S.C., Brooks, Stephen P., Catchpole, Edward A., Morgan, Byron J. T. (2003) The analysis of ring-recovery data using random effects. Biometrics, 59 (1). pp. 54-65. ISSN 0006-341X. (doi:10.1111/1541-0420.00007) (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided) (KAR id:10485)
The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided. | |
Official URL: http://dx.doi.org/10.1111/1541-0420.00007 |
Abstract
We show how random terms, describing both yearly variation and overdispersion, can easily be incorporated into models for mark-recovery data, through the use of Bayesian methods. For recovery data on lapwings, we show that the incorporation of the random terms greatly improves the goodness of fit. Omitting the random terms can lead to overestimation of the significance of weather on survival, and overoptimistic prediction intervals in simulations of future population behavior. Random effects models provide a natural way of modeling overdispersion-which is more satisfactory than the standard classical approach of scaling up all standard errors by a uniform inflation factor. We compare models by means of Bayesian a-values and the deviance information criterion (DIC).
Item Type: | Article |
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DOI/Identification number: | 10.1111/1541-0420.00007 |
Uncontrolled keywords: | AIC; band-return; Bayesian p-values; DIC; lapwings; MCMC LAPWINGS VANELLUS-VANELLUS; SURVIVAL ESTIMATION; BAYESIAN-ANALYSIS; MARKED ANIMALS; MODELS; RATES |
Subjects: | Q Science > QA Mathematics (inc Computing science) |
Divisions: | Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Mathematics, Statistics and Actuarial Science |
Depositing User: | Judith Broom |
Date Deposited: | 09 Sep 2008 06:33 UTC |
Last Modified: | 16 Nov 2021 09:49 UTC |
Resource URI: | https://kar.kent.ac.uk/id/eprint/10485 (The current URI for this page, for reference purposes) |
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