A general class of recapture models based on the conditional capture probabilities.
We propose an Mhotb 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.
Department of Public Health and Infectious Diseases, Sapienza - University of Rome.
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Farcomeni
D000465:Algorithms; D000818:Animals; D001699:Biometry / Q000379:methods; D019352:Censuses; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D015987:Effect Modifier, Epidemiologic; D006801:Humans; D016013:Likelihood Functions; D015233:Models, Statistical; D011156:Population Density; D015203:Reproducibility of Results; D018401:Sample Size; D012680:Sensitivity and Specificity
Aitchinson-Silvey algorithm; Capture history; Equality constraints; Heterogeneity; Population size