Show simple item record

Files in this item


Item metadata

dc.contributor.authorLangrock, Roland
dc.contributor.authorKing, Ruth
dc.identifier.citationLangrock , R & King , R 2013 , ' Maximum likelihood estimation of mark-recapture-recovery models in the presence of continuous covariates ' , Annals of Applied Statistics , vol. 7 , no. 3 , pp. 1709-1732 .
dc.identifier.otherPURE: 28450207
dc.identifier.otherPURE UUID: c92351bb-8671-4934-90fb-e19cffb4d445
dc.identifier.otherScopus: 84885046625
dc.descriptionSupplementary material: R code for model fitting. Sample R code for simulating MRR data and fitting the corresponding model using the HMM-based approach (with MRR model as described in Section 3). Digital Object Identifier: doi:10.1214/13-AOAS644SUPPen
dc.description.abstractWe consider mark-recapture-recovery (MRR) data of animals where the model parameters are a function of individual time-varying continuous covariates. For such covariates, the covariate value is unobserved if the corresponding individual is unobserved, in which case the survival probability cannot be evaluated. For continuous-valued covariates, the corresponding likelihood can only be expressed in the form of an integral that is analytically intractable, and, to date, no maximum likelihood approach that uses all the information in the data has been developed. Assuming a first-order Markov process for the covariate values, we accomplish this task by formulating the MRR setting in a state-space framework and considering an approximate likelihood approach which essentially discretizes the range of covariate values, reducing the integral to a summation. The likelihood can then be efficiently calculated and maximized using standard techniques for hidden Markov models. We initially assess the approach using simulated data before applying to real data relating to Soay sheep, specifying the survival probability as a function of body mass. Models that have previously been suggested for the corresponding covariate process are typically of the form of di.usive random walks. We consider an alternative non-di.usive AR(1)-type model which appears to provide a significantly better fit to the Soay sheep data.
dc.relation.ispartofAnnals of Applied Statisticsen
dc.rightsCopyright (c) 2013 Institute of Mathematical Statisticsen
dc.subjectArnason-Schwarz modelen
dc.subjectHidden Markov modelen
dc.subjectMarkov chainen
dc.subjectMissing valuesen
dc.subjectSoay sheepen
dc.subjectState-space modelen
dc.subjectQA Mathematicsen
dc.titleMaximum likelihood estimation of mark-recapture-recovery models in the presence of continuous covariatesen
dc.typeJournal articleen
dc.contributor.institutionUniversity of St Andrews. School of Mathematics and Statisticsen
dc.contributor.institutionUniversity of St Andrews. Scottish Oceans Instituteen
dc.contributor.institutionUniversity of St Andrews. Centre for Research into Ecological & Environmental Modellingen
dc.description.statusPeer revieweden

This item appears in the following Collection(s)

Show simple item record