Continuous‐time capture–recapture in closed populations. Issue 2 (12th September 2017)

• Record Type: Journal Article
• Authors: Schofield, Matthew R.; Barker, Richard J.; Gelling, Nicholas
• Is Part Of: Biometrics. Volume 74:Issue 2(2018)
• Page Start: 626
• Page End: 635
• Publication Date: 2017-09-12
• Subjects: Capture–recapture -- Likelihood factorization -- Markov chain Monte Carlo -- Nonhomogenous Poisson process; Biometry -- Periodicals; 570.15195
• DOI: 10.1111/biom.12763
• Languages: English
• ISSNs: 0006-341X

Abstract

Summary: The standard approach to fitting capture–recapture data collected in continuous time involves arbitrarily forcing the data into a series of distinct discrete capture sessions. We show how continuous‐time models can be fitted as easily as discrete‐time alternatives. The likelihood is factored so that efficient Markov chain Monte Carlo algorithms can be implemented for Bayesian estimation, available online in the R packagectime . We consider goodness‐of‐fit tests for behavior and heterogeneity effects as well as implementing models that allow for such effects.