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Parameter Redundancy in Multistate Capture‐Recapture Models
Author(s) -
Gimenez Olivier,
Choquet Rémi,
Lebreton JeanDominique
Publication year - 2003
Publication title -
biometrical journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.108
H-Index - 63
eISSN - 1521-4036
pISSN - 0323-3847
DOI - 10.1002/bimj.200390043
Subject(s) - mark and recapture , parameterized complexity , model selection , redundancy (engineering) , computer science , selection (genetic algorithm) , statistics , machine learning , mathematics , algorithm , population , demography , sociology , operating system
Multistate capture‐recapture models are a powerful tool to address a variety of biological questions concerning dispersal and/or individual variability in wild animal populations. However, biologically meaningful models are often over‐parameterized and consequently some parameters cannot be estimated separately. Identifying which quantities are separately estimable is crucial for proper model selection based upon likelihood tests or information criteria and for the interpretation of the estimates obtained. We show how to investigate parameter redundancy in multistate capture‐recapture models, based on formal methods initially proposed by Catchpole and his associates for exponential family distributions (Catchpole, Freeman and Morgan, 1996. Journal of the Royal Statistical Society Series B 58, 763–774). We apply their approach to three models of increasing complexity.