Analysing mark-recapture-recovery data in the presence of missing covariate data via multiple imputation

Research output: Contribution to journalArticlepeer-review

6 Citations (Scopus)
1 Downloads (Pure)


We consider mark–recapture–recovery data with additional individual time-varying continuous covariate data. For such data it is common to specify the model parameters, and in particular the survival probabilities, as a function of these covariates to incorporate individual heterogeneity. However, an issue arises in relation to missing covariate values, for (at least) the times when an individual is not observed, leading to an analytically intractable likelihood. We propose a two-step multiple imputation approach to obtain estimates of the demographic parameters. Firstly, a model is fitted to only the observed covariate values. Conditional on the fitted covariate model, multiple “complete” datasets are generated (i.e. all missing covariate values are imputed). Secondly, for each complete dataset, a closed form complete data likelihood can be maximised to obtain estimates of the model parameters which are subsequently combined to obtain an overall estimate of the parameters. Associated standard errors and 95 % confidence intervals are obtained using a non-parametric bootstrap. A simulation study is undertaken to assess the performance of the proposed two-step approach. We apply the method to data collected on a well-studied population of Soay sheep and compare the results with a Bayesian data augmentation approach. Supplementary materials accompanying this paper appear on-line.
Original languageEnglish
Pages (from-to)28-46
JournalJournal of Agricultural, Biological and Environmental Statistics
Issue number1
Early online date9 Dec 2014
Publication statusPublished - Mar 2015


  • Individual time-varying
  • Continuous covariates
  • Mark-recapture-recovery data
  • Missing values
  • Multiple imputation
  • Two-step algorithm


Dive into the research topics of 'Analysing mark-recapture-recovery data in the presence of missing covariate data via multiple imputation'. Together they form a unique fingerprint.

Cite this