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

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Analysing mark-recapture-recovery data in the presence of missing covariate data via multiple imputation. / Worthington, Hannah; King, Ruth; Buckland, Stephen Terrence.

In: Journal of Agricultural, Biological and Environmental Statistics, Vol. 20, No. 1, 03.2015, p. 28-46.

Research output: Contribution to journalArticle

Harvard

Worthington, H, King, R & Buckland, ST 2015, 'Analysing mark-recapture-recovery data in the presence of missing covariate data via multiple imputation' Journal of Agricultural, Biological and Environmental Statistics, vol. 20, no. 1, pp. 28-46. https://doi.org/10.1007/s13253-014-0184-z

APA

Worthington, H., King, R., & Buckland, S. T. (2015). Analysing mark-recapture-recovery data in the presence of missing covariate data via multiple imputation. Journal of Agricultural, Biological and Environmental Statistics, 20(1), 28-46. https://doi.org/10.1007/s13253-014-0184-z

Vancouver

Worthington H, King R, Buckland ST. Analysing mark-recapture-recovery data in the presence of missing covariate data via multiple imputation. Journal of Agricultural, Biological and Environmental Statistics. 2015 Mar;20(1):28-46. https://doi.org/10.1007/s13253-014-0184-z

Author

Worthington, Hannah ; King, Ruth ; Buckland, Stephen Terrence. / Analysing mark-recapture-recovery data in the presence of missing covariate data via multiple imputation. In: Journal of Agricultural, Biological and Environmental Statistics. 2015 ; Vol. 20, No. 1. pp. 28-46.

Bibtex - Download

@article{5b15ca7d165048889926fd82c240441e,
title = "Analysing mark-recapture-recovery data in the presence of missing covariate data via multiple imputation",
abstract = "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.",
keywords = "Individual time-varying, Continuous covariates, Mark-recapture-recovery data, Missing values, Multiple imputation, Two-step algorithm",
author = "Hannah Worthington and Ruth King and Buckland, {Stephen Terrence}",
year = "2015",
month = "3",
doi = "10.1007/s13253-014-0184-z",
language = "English",
volume = "20",
pages = "28--46",
journal = "Journal of Agricultural, Biological and Environmental Statistics",
issn = "1085-7117",
publisher = "Springer",
number = "1",

}

RIS (suitable for import to EndNote) - Download

TY - JOUR

T1 - Analysing mark-recapture-recovery data in the presence of missing covariate data via multiple imputation

AU - Worthington, Hannah

AU - King, Ruth

AU - Buckland, Stephen Terrence

PY - 2015/3

Y1 - 2015/3

N2 - 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.

AB - 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.

KW - Individual time-varying

KW - Continuous covariates

KW - Mark-recapture-recovery data

KW - Missing values

KW - Multiple imputation

KW - Two-step algorithm

U2 - 10.1007/s13253-014-0184-z

DO - 10.1007/s13253-014-0184-z

M3 - Article

VL - 20

SP - 28

EP - 46

JO - Journal of Agricultural, Biological and Environmental Statistics

T2 - Journal of Agricultural, Biological and Environmental Statistics

JF - Journal of Agricultural, Biological and Environmental Statistics

SN - 1085-7117

IS - 1

ER -

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ID: 467969