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Article: Timevarying covariates and semiparametric regression in capturerecapture: an adaptive spline approach
Title  Timevarying covariates and semiparametric regression in capturerecapture: an adaptive spline approach 

Authors  
Keywords  Adaptive splines Bayesian inference Capture–recapture Freeknot spines Reversiblejump Markov chain Monte Carlo 
Issue Date  2009 
Publisher  Springer New York LLC. The Journal's web site is located at http://springerlink.metapress.com/openurl.asp?genre=journal&issn=13528505 
Citation  Environmental and Ecological Statistics, 2009, v. 3, p. 657675 How to Cite? 
Abstract  Advances in capture–recapture methodology have allowed the inclusion of continuous, timedependent individualcovariates as predictors of survival and capture probabilities. The problem posed by these covariates is that they are only observed for an individual when that individual is captured. One solution is to assume a model of the covariate which defines the distribution of unobserved values,
conditional on the observed values, and apply Bayesian methods to compute parameter estimates and to test the covariate’s effect.
Previous applications of this approach have modeled the survival probability as a linear function of the covariate on some scale (e.g. identity or logistic). In some applications a linear function may not adequately describe the true relationship. Here we incorporate semiparametric regression to allow for more flexibility in the relationship between the covariate and the survival probabilities of the Cormack–
Jolly–Seber model. A fully Bayesian, adaptive algorithm is used to model the relationship with splines, in which the complexity of the relationship is governed by the number and location of the knots in the spline. A reversible jump Markov chain Monte Carlo algorithm is implemented to explore splines with different knot configurations, and model averaging is used to compute the final estimates of the survival
probabilities.
The method is applied to a simulated data set and to data collected through the Dutch Constant Effort Sites ringing project to study the survival of reed warblers (Acrocephalus scirpaceus) as a function of condition. 
Persistent Identifier  http://hdl.handle.net/10722/127424 
ISSN  2015 Impact Factor: 0.769 2015 SCImago Journal Rankings: 0.516 
DC Field  Value  Language 

dc.contributor.author  Bonner, SJ  en_HK 
dc.contributor.author  Thomson, DL  en_HK 
dc.contributor.author  Schwarz, CJ  en_HK 
dc.date.accessioned  20101031T13:24:50Z   
dc.date.available  20101031T13:24:50Z   
dc.date.issued  2009  en_HK 
dc.identifier.citation  Environmental and Ecological Statistics, 2009, v. 3, p. 657675  en_HK 
dc.identifier.issn  13528505   
dc.identifier.uri  http://hdl.handle.net/10722/127424   
dc.description.abstract  Advances in capture–recapture methodology have allowed the inclusion of continuous, timedependent individualcovariates as predictors of survival and capture probabilities. The problem posed by these covariates is that they are only observed for an individual when that individual is captured. One solution is to assume a model of the covariate which defines the distribution of unobserved values, conditional on the observed values, and apply Bayesian methods to compute parameter estimates and to test the covariate’s effect. Previous applications of this approach have modeled the survival probability as a linear function of the covariate on some scale (e.g. identity or logistic). In some applications a linear function may not adequately describe the true relationship. Here we incorporate semiparametric regression to allow for more flexibility in the relationship between the covariate and the survival probabilities of the Cormack– Jolly–Seber model. A fully Bayesian, adaptive algorithm is used to model the relationship with splines, in which the complexity of the relationship is governed by the number and location of the knots in the spline. A reversible jump Markov chain Monte Carlo algorithm is implemented to explore splines with different knot configurations, and model averaging is used to compute the final estimates of the survival probabilities. The method is applied to a simulated data set and to data collected through the Dutch Constant Effort Sites ringing project to study the survival of reed warblers (Acrocephalus scirpaceus) as a function of condition.   
dc.language  eng  en_HK 
dc.publisher  Springer New York LLC. The Journal's web site is located at http://springerlink.metapress.com/openurl.asp?genre=journal&issn=13528505   
dc.relation.ispartof  Environmental and Ecological Statistics  en_HK 
dc.rights  Creative Commons: Attribution 3.0 Hong Kong License   
dc.rights  The original publication is available at www.springerlink.com   
dc.subject  Adaptive splines   
dc.subject  Bayesian inference   
dc.subject  Capture–recapture   
dc.subject  Freeknot spines   
dc.subject  Reversiblejump Markov chain Monte Carlo   
dc.title  Timevarying covariates and semiparametric regression in capturerecapture: an adaptive spline approach  en_HK 
dc.type  Article  en_HK 
dc.identifier.openurl  http://library.hku.hk:4550/resserv?sid=HKU:IR&issn=13528505&volume=3&spage=657&epage=675&date=2009&atitle=TimeVarying+Covariates+and+SemiParametric+Regression+in+CaptureRecapture:+an+Adaptive+Spline+Approach   
dc.identifier.email  Bonner, SJ: sbonner@stat.sfu.ca  en_HK 
dc.identifier.email  Thomson, DL: dthomson@hku.hk   
dc.identifier.email  Schwarz, CJ: cschwarz@stat.sfu.ca   
dc.identifier.authority  Thomson, DL=rp00788  en_HK 
dc.description.nature  postprint   
dc.identifier.doi  10.1007/9780387781518_29   
dc.identifier.hkuros  174390  en_HK 
dc.identifier.volume  3  en_HK 
dc.identifier.spage  657  en_HK 
dc.identifier.epage  675  en_HK 