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May 2016 Multivariate State Hidden Markov Models for Mark-Recapture Data
Devin S. Johnson, Jeff L. Laake, Sharon R. Melin, Robert L. DeLong
Statist. Sci. 31(2): 233-244 (May 2016). DOI: 10.1214/15-STS542


State-based Cormack–Jolly–Seber (CJS) models have become an often used method for assessing states or conditions of free-ranging animals through time. Although originally envisioned to account for differences in survival and observation processes when animals are moving though various geographical strata, the model has evolved to model vital rates in different life-history or diseased states. We further extend this useful class of models to the case of multivariate state data. Researchers can record values of several different states of interest, for example, geographic location and reproductive state. Traditionally, these would be aggregated into one state with a single probability of state uncertainty. However, by modeling states as a multivariate vector, one can account for partial knowledge of the vector as well as dependence between the state variables in a parsimonious way. A hidden Markov model (HMM) formulation allows straightforward maximum likelihood inference. The proposed HMM models are demonstrated with a case study using data from a California sea lion vital rates study.


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Devin S. Johnson. Jeff L. Laake. Sharon R. Melin. Robert L. DeLong. "Multivariate State Hidden Markov Models for Mark-Recapture Data." Statist. Sci. 31 (2) 233 - 244, May 2016.


Published: May 2016
First available in Project Euclid: 24 May 2016

zbMATH: 06946224
MathSciNet: MR3506102
Digital Object Identifier: 10.1214/15-STS542

Rights: Copyright © 2016 Institute of Mathematical Statistics


Vol.31 • No. 2 • May 2016
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