On the Bayesian analysis of ring-recovery data.
Vounatsou and Smith (1995, Biometrics 51, 687-708) describe the modern Bayesian analysis of ring-recovery data. Here we discuss and extend their work. We draw different conclusions from two major data analyses. We emphasize the extreme sensitivity of certain parameter estimates to the choice of prior distribution and conclude that naive use of Bayesian methods in this area can be misleading. Additionally, we explain the discrepancy between the Bayesian and classical analyses when the likelihood surface has a flat ridge. In this case, when there is no unique maximum likelihood estimate, the Bayesian estimators are remarkably precise.
Brooks
S P
SP
Department of Mathematics and Statistics, University of Surrey, Guildford, England. steve@statslab.cam.ac.uk
Catchpole
E A
EA
Morgan
B J
BJ
Barry
S C
SC
D000818:Animals; D001499:Bayes Theorem; D001717:Birds; D004372:Ducks; D015233:Models, Statistical; D011156:Population Density; D011336:Probability; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity