A new strategy for diagnostic model assessment in capture-recapture

McCrea, Rachel and Morgan, Byron J. T. and Gimenez, Olivier (2017) A new strategy for diagnostic model assessment in capture-recapture. Journal of the Royal Statistical Society: Series C (Applied Statistics), 66 (4). pp. 815-831. ISSN 0035-9254

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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:
Journal Article
Journal or Publication Title:
Journal of the Royal Statistical Society: Series C (Applied Statistics)
Uncontrolled Keywords:
/dk/atira/pure/subjectarea/asjc/1800/1804
Subjects:
ID Code:
176513
Deposited By:
Deposited On:
12 Oct 2022 11:25
Refereed?:
Yes
Published?:
Published
Last Modified:
22 Nov 2022 11:52