The Use of Mixed Logit Models to Reflect Heterogeneity in Capture-Recapture Studies

We examine issues in estimating population size N with capture-recapture models when there is variable catchability among subjects. We focus on a logistic-normal mixed model, for which the logit of the probability of capture is an additive function of a random subject and a fixed sampling occasion p...

Full description

Saved in:
Bibliographic Details
Published in:Biometrics 1999-03, Vol.55 (1), p.294-301
Main Authors: Coull, Brent A., Agresti, Alan
Format: Article
Language:English
Subjects:
Animals
Biometrics
Biometry
Chaos theory
Confidence interval
Consultant's Forum
Generalized linear mixed model
Interval estimators
Lagomorpha
Latent class model
Linear Models
Log-linear model
Logistic Models
Modeling
Models, Statistical
Musical intervals
Point estimators
Population Density
Population estimates
Population size
Profile likelihood
Quasi symmetry
Rasch model
Simulations
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:We examine issues in estimating population size N with capture-recapture models when there is variable catchability among subjects. We focus on a logistic-normal mixed model, for which the logit of the probability of capture is an additive function of a random subject and a fixed sampling occasion parameter. When the probability of capture is small or the degree of heterogeneity is large, the log-likelihood surface is relatively flat and it is difficult to obtain much information about N. We also discuss a latent class model and a log-linear model that account for heterogeneity and show that the log-linear model has greater scope. Models assuming homogeneity provide much narrower intervals for N but are usually highly overly optimistic, the actual coverage probability being much lower than the nominal level.
ISSN:0006-341X
1541-0420
DOI:10.1111/j.0006-341X.1999.00294.x