Estimation of survival and capture probabilities in open population capture–recapture models when covariates are subject to measurement error
Predictor variables (or covariates) are frequently used in a capture–recapture analysis when estimating demographic quantities such as population size or survival probabilities. If these predictor variables are measured with error and subsequently used in the analysis, then estimates of the model pa...
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| Published in | Computational statistics & data analysis Vol. 96; pp. 74 - 86 |
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| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
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Elsevier B.V
01.04.2016
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| Abstract | Predictor variables (or covariates) are frequently used in a capture–recapture analysis when estimating demographic quantities such as population size or survival probabilities. If these predictor variables are measured with error and subsequently used in the analysis, then estimates of the model parameters may be biased. Several approaches have been proposed to account for error-in-variables in capture–recapture models, however these methods generally assume the population is closed; hence quantities of interest for open populations such as the survival probabilities do not appear in the likelihood. To account for measurement error in environmental time-varying covariates for open population capture–recapture data, the well-known Cormack–Jolly–Seber model and two statistical methods are considered: (1) simulation–extrapolation; and (2) regression calibration, as well as a new method which accounts for correlation (arising from measurement error) between the survival and capture probabilities. Several simulation studies are conducted to examine the method performances, and a case study is presented which uses capture–recapture data on the Little Penguin Eudyptula minor and sea-surface temperature data as an environmental covariate to model their survival and capture probabilities. |
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| AbstractList | Predictor variables (or covariates) are frequently used in a capture–recapture analysis when estimating demographic quantities such as population size or survival probabilities. If these predictor variables are measured with error and subsequently used in the analysis, then estimates of the model parameters may be biased. Several approaches have been proposed to account for error-in-variables in capture–recapture models, however these methods generally assume the population is closed; hence quantities of interest for open populations such as the survival probabilities do not appear in the likelihood. To account for measurement error in environmental time-varying covariates for open population capture–recapture data, the well-known Cormack–Jolly–Seber model and two statistical methods are considered: (1) simulation–extrapolation; and (2) regression calibration, as well as a new method which accounts for correlation (arising from measurement error) between the survival and capture probabilities. Several simulation studies are conducted to examine the method performances, and a case study is presented which uses capture–recapture data on the Little Penguin Eudyptula minor and sea-surface temperature data as an environmental covariate to model their survival and capture probabilities. Predictor variables (or covariates) are frequently used in a capture–recapture analysis when estimating demographic quantities such as population size or survival probabilities. If these predictor variables are measured with error and subsequently used in the analysis, then estimates of the model parameters may be biased. Several approaches have been proposed to account for error-in-variables in capture–recapture models, however these methods generally assume the population is closed; hence quantities of interest for open populations such as the survival probabilities do not appear in the likelihood. To account for measurement error in environmental time-varying covariates for open population capture–recapture data, the well-known Cormack–Jolly–Seber model and two statistical methods are considered: (1) simulation–extrapolation; and (2) regression calibration, as well as a new method which accounts for correlation (arising from measurement error) between the survival and capture probabilities. Several simulation studies are conducted to examine the method performances, and a case study is presented which uses capture–recapture data on the Little Penguin Eudyptula minor and sea-surface temperature data as an environmental covariate to model their survival and capture probabilities. |
| Author | Huggins, Richard M. Dann, Peter Hwang, Wen-Han Stoklosa, Jakub |
| Author_xml | – sequence: 1 givenname: Jakub surname: Stoklosa fullname: Stoklosa, Jakub email: j.stoklosa@unsw.edu.au organization: School of Mathematics and Statistics and Evolution & Ecology Research Centre, The University of New South Wales, New South Wales 2052, Australia – sequence: 2 givenname: Peter surname: Dann fullname: Dann, Peter organization: Research Department, Phillip Island Nature Parks, Victoria 3922, Australia – sequence: 3 givenname: Richard M. surname: Huggins fullname: Huggins, Richard M. organization: Department of Mathematics and Statistics, The University of Melbourne, Victoria 3010, Australia – sequence: 4 givenname: Wen-Han surname: Hwang fullname: Hwang, Wen-Han email: wenhan@nchu.edu.tw organization: Institute of Statistics and Department of Applied Mathematics, National Chung Hsing University, Taichung 402, Taiwan |
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| Keywords | Mark–capture–recapture SIMEX Regression calibration Cormack–Jolly–Seber model Error-in-variables Little Penguins |
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| SubjectTerms | calibration case studies Cormack–Jolly–Seber model Error-in-variables Eudyptula minor Little Penguins Mark–capture–recapture population size Regression calibration SIMEX statistical analysis surface water temperature |
| Title | Estimation of survival and capture probabilities in open population capture–recapture models when covariates are subject to measurement error |
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