Open Access
March 2016 Extending the latent multinomial model with complex error processes and dynamic Markov bases
Simon J. Bonner, Matthew R. Schofield, Patrik Noren, Steven J. Price
Ann. Appl. Stat. 10(1): 246-263 (March 2016). DOI: 10.1214/15-AOAS889


The latent multinomial model (LMM) of Link et al. [Biometrics 66 (2010) 178–185] provides a framework for modelling mark-recapture data with potential identification errors. Key is a Markov chain Monte Carlo (MCMC) scheme for sampling configurations of the latent counts of the true capture histories that could have generated the observed data. Assuming a linear map between the observed and latent counts, the MCMC algorithm uses vectors from a basis of the kernel to move between configurations of the latent data. Schofield and Bonner [Biometrics 71 (2015) 1070–1080] shows that this is sufficient for some models within the framework but that a larger set called a Markov basis is required when errors are more complex. We address two further challenges: (1) that models with complex error mechanisms may not fit within the LMM framework and (2) that Markov bases can be difficult to compute for studies of even moderate size. We extend the framework to model the capture/demographic and error processes separately and develop a new MCMC algorithm using dynamic Markov bases. Our work is motivated by a study of queen snakes (Regina septemvittata) and we use simulation to compare estimates of survival rates when snakes are marked with PIT tags which have perfect identification versus brands which are prone to error.


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Simon J. Bonner. Matthew R. Schofield. Patrik Noren. Steven J. Price. "Extending the latent multinomial model with complex error processes and dynamic Markov bases." Ann. Appl. Stat. 10 (1) 246 - 263, March 2016.


Received: 1 April 2015; Revised: 1 September 2015; Published: March 2016
First available in Project Euclid: 25 March 2016

zbMATH: 06586144
MathSciNet: MR3480495
Digital Object Identifier: 10.1214/15-AOAS889

Keywords: Bayesian inference , Markov basis , Markov chain Monte Carlo , mark-recapture , misidentification , queen snake (Regina septemvittata)

Rights: Copyright © 2016 Institute of Mathematical Statistics

Vol.10 • No. 1 • March 2016
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