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Semiparametric Regression in Capture–Recapture Modeling
Author(s) -
Gimenez O.,
Crainiceanu C.,
Barbraud C.,
Jenouvrier S.,
Morgan B. J. T.
Publication year - 2006
Publication title -
biometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.298
H-Index - 130
eISSN - 1541-0420
pISSN - 0006-341X
DOI - 10.1111/j.1541-0420.2005.00514.x
Subject(s) - mark and recapture , covariate , markov chain monte carlo , bayesian probability , statistics , econometrics , nonparametric statistics , computer science , mathematics , population , demography , sociology
Summary Capture–recapture models were developed to estimate survival using data arising from marking and monitoring wild animals over time. Variation in survival may be explained by incorporating relevant covariates. We propose nonparametric and semiparametric regression methods for estimating survival in capture–recapture models. A fully Bayesian approach using Markov chain Monte Carlo simulations was employed to estimate the model parameters. The work is illustrated by a study of Snow petrels, in which survival probabilities are expressed as nonlinear functions of a climate covariate, using data from a 40‐year study on marked individuals, nesting at Petrels Island, Terre Adélie.