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A General Class of Recapture Models Based on the Conditional Capture Probabilities

A General Class of Recapture Models Based on the Conditional Capture Probabilities SummaryWe propose an model for population size estimation in capture-recapture studies. The tb part is based on equality constraints for the conditional capture probabilities, leading to an extremely rich model class. Observed and unobserved heterogeneity are dealt with by means of a logistic parameterization. In order to explore the model class, we introduce a penalized version of the likelihood. The conditional likelihood and penalized conditional likelihood are maximized by means of efficient EM algorithms. Simulations and two real data examples illustrate the approach. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Biometrics Oxford University Press

A General Class of Recapture Models Based on the Conditional Capture Probabilities

Biometrics , Volume 72 (1): 9 – Sep 10, 2015

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References (39)

Publisher
Oxford University Press
Copyright
© 2015, The International Biometric Society
ISSN
0006-341X
eISSN
1541-0420
DOI
10.1111/biom.12375
Publisher site
See Article on Publisher Site

Abstract

SummaryWe propose an model for population size estimation in capture-recapture studies. The tb part is based on equality constraints for the conditional capture probabilities, leading to an extremely rich model class. Observed and unobserved heterogeneity are dealt with by means of a logistic parameterization. In order to explore the model class, we introduce a penalized version of the likelihood. The conditional likelihood and penalized conditional likelihood are maximized by means of efficient EM algorithms. Simulations and two real data examples illustrate the approach.

Journal

BiometricsOxford University Press

Published: Sep 10, 2015

Keywords: Aitchinson-Silvey algorithm; Capture history; Equality constraints; Heterogeneity; Population size

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