Catchpole, E.A. and Morgan, B.J.T. and Tavecchia, G. (2008) A new method for analysing discrete life history data with missing covariate values. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 70 (2). pp. 445-460. ISSN 1369-7412.
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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 likleihood 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 then alternative methods when there are missing covariate data.
|Uncontrolled keywords:||complete-case analysis; life history data; maximum likelihood; missing data; renewal process; survival analysis; time varying individual covariates; trinomial distribution|
|Subjects:||Q Science > QA Mathematics (inc Computing science) > QA276 Mathematical statistics|
|Divisions:||Faculties > Science Technology and Medical Studies > School of Mathematics Statistics and Actuarial Science|
|Depositing User:||Byron Morgan|
|Date Deposited:||14 May 2008 08:29|
|Last Modified:||14 Jan 2010 14:11|
|Resource URI:||http://kar.kent.ac.uk/id/eprint/3173 (The current URI for this page, for reference purposes)|
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