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A new method for analysing discrete life history data with missing covariate values

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Regular censusing of wild animal populations produces data for estimating their annual survival. However, there can be missing covariate data; for instance time varying covariates that are measured on individual animals often contain missing values. By considering the transitions that occur from each occasion to the next, we derive a novel expression for the likelihood for mark–recapture–recovery data, which is equivalent to the traditional likelihood in the case where no covariate data are missing, and which provides a natural way of dealing with covariate data that are missing, for whatever reason. Unlike complete-case analysis, this approach does not exclude incompletely observed life histories, uses all available data and produces consistent estimators. In a simulation study it performs better overall than alternative methods when there are missing covariate data.

Keywords: Complete-case analysis; Life history data; Maximum likelihood; Missing data; Renewal process; Survival analysis; Time varying individual covariates; Trinomial distribution

Document Type: Research Article


Affiliations: 1: University of New South Wales at the Australian Defence Force Academy, Canberra, Australia, and University of Kent, Canterbury, UK 2: University of Kent, Canterbury, UK

Publication date: April 1, 2008


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