| Abstract: | Data augmentation (DA) is a flexible tool for analyzing closed and open population models of capture-recapture data, especially models which include sources of hetereogeneity among individuals. The essential concept underlying DA, as we use the term, is based on adding "observations" to create a dataset composed of a known number of individuals. This new (augmented) dataset, which includes the unknown number of individuals N in the population, is then analyzed using a new model that includes a reformulation of the parameter N in the conventional model of the observed (unaugmented) data. In the context of capture-recapture models, we add a set of "all zero" encounter histories which are not, in practice, observable. The model of the augmented dataset is a zero-inflated version of either a binomial or a multinomial base model. Thus, our use of DA provides a general approach for analyzing both closed and open population models of all types. In doing so, this approach provides a unified framework for the analysis of a huge range of models that are treated as unrelated "black boxes" and named procedures in the classical literature. As a practical matter, analysis of the augmented dataset by MCMC is greatly simplified compared to other methods that require specialized algorithms. For example, complex capture-recapture models of an augmented dataset can be fitted with popular MCMC software packages (WinBUGS or JAGS) by providing a concise statement of the model‘s assumptions that usually involves only a few lines of pseudocode. In this paper, we review the basic technical concepts of data augmentation, and we provide examples of analyses of closed-population models (M 0, M h , distance sampling, and spatial capture-recapture models) and open-population models (Jolly-Seber) with individual effects. |
| Genre: | Article |
| ProdID: | 70038056 |
| Citation Author: | Royle, J. Andrew; Dorazio, Robert M. |
| Citation Contributing Office: | Patuxent Wildlife Research Center |
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| Citation End Page: | 537 |
| Citation Issue: | Supplement 2 |
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| Citation Language: | English |
| Citation Larger Work Title: | Journal of Ornithology |
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| Citation Number Of Pages: | 7 |
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| Citation Phsyical Description: | 17 p. |
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| Citation Publisher: | Springer |
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| Citation Search Results Text: | Parameter-expanded data augmentation for Bayesian analysis of capture-recapture models; 2012; Article; Journal; Journal of Ornithology; Royle, J. Andrew; Dorazio, Robert M. |
| Citation Start Page: | 521 |
| Citation Volume: | 152 |
| Citation Year: | 2012 |
| Type: | citation/reference |
| Text: | Parameter-expanded data augmentation for Bayesian analysis of capture-recapture models; 2012; Article; Journal; Journal of Ornithology; Royle, J. Andrew; Dorazio, Robert M. |
| URL (THUMBNAIL): | http://pubs.er.usgs.gov/thumbnails/outside_thumb.jpg |
| URL (DIGITAL OBJECT IDENTIFIER): | http://dx.doi.org/10.1007/s10336-010-0619-4 |
| Date Other: | Mon, 16 Apr 2012 00:00 -0500 |
| Publisher: | Springer |
