A new strategy for diagnostic model assessment in capture-recapture

McCrea, Rachel S. and Morgan, Byron J. T. and Gimenez, Olivier (2016) A new strategy for diagnostic model assessment in capture-recapture. Journal of the Royal Statistical Society: Series C (Applied Statistics), . ISSN 0035-9254. (doi:https://doi.org/10.1111/rssc.12197) (Full text available)

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https://doi.org/10.1111/rssc.12197

Abstract

Common to both diagnostic tests used in capture–recapture and score tests is the idea that starting from a simple base model it is possible to interrogate data to determine whether more complex parameter structures will be supported. Current recommendations advise that diagnostic tests are performed as a precursor to a model selection step. We show that certain well-known diagnostic tests for examining the fit of capture–recapture models to data are in fact score tests. Because of this direct relationship we investigate a new strategy for model assessment which combines the diagnosis of departure from basic model assumptions with a step-up model selection, all based on score tests. We investigate the power of such an approach to detect common reasons for lack of model fit and compare the performance of this new strategy with the existing recommendations by using simulation. We present motivating examples with real data for which the extra flexibility of score tests results in an improved performance compared with diagnostic tests.

Item Type: Article
Uncontrolled keywords: Goodness-of-fit tests; Model selection; Power; Transience; Trap dependence; U-CARE
Divisions: Faculties > Sciences > School of Mathematics Statistics and Actuarial Science
Depositing User: Matthias Werner
Date Deposited: 28 Feb 2017 09:54 UTC
Last Modified: 28 Feb 2017 10:51 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/60585 (The current URI for this page, for reference purposes)
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