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A new strategy for diagnostic model assessment in capture-recapture

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A new strategy for diagnostic model assessment in capture-recapture. / McCrea, Rachel; Morgan, Byron J. T.; Gimenez, Olivier.
In: Journal of the Royal Statistical Society: Series C (Applied Statistics), Vol. 66, No. 4, 08.07.2017, p. 815-831.

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Harvard

McCrea, R, Morgan, BJT & Gimenez, O 2017, 'A new strategy for diagnostic model assessment in capture-recapture', Journal of the Royal Statistical Society: Series C (Applied Statistics), vol. 66, no. 4, pp. 815-831. https://doi.org/10.1111/rssc.12197

APA

McCrea, R., Morgan, B. J. T., & Gimenez, O. (2017). A new strategy for diagnostic model assessment in capture-recapture. Journal of the Royal Statistical Society: Series C (Applied Statistics), 66(4), 815-831. https://doi.org/10.1111/rssc.12197

Vancouver

McCrea R, Morgan BJT, Gimenez O. A new strategy for diagnostic model assessment in capture-recapture. Journal of the Royal Statistical Society: Series C (Applied Statistics). 2017 Jul 8;66(4):815-831. Epub 2016 Nov 17. doi: 10.1111/rssc.12197

Author

McCrea, Rachel ; Morgan, Byron J. T. ; Gimenez, Olivier. / A new strategy for diagnostic model assessment in capture-recapture. In: Journal of the Royal Statistical Society: Series C (Applied Statistics). 2017 ; Vol. 66, No. 4. pp. 815-831.

Bibtex

@article{027af8383ea84073ad696f7352633451,
title = "A new strategy for diagnostic model assessment in capture-recapture",
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.",
keywords = "Goodness-of-fit tests, Model selection, Power, Transience, Trap dependence, U-CARE",
author = "Rachel McCrea and Morgan, {Byron J. T.} and Olivier Gimenez",
year = "2017",
month = jul,
day = "8",
doi = "10.1111/rssc.12197",
language = "English",
volume = "66",
pages = "815--831",
journal = "Journal of the Royal Statistical Society: Series C (Applied Statistics)",
issn = "0035-9254",
publisher = "Wiley-Blackwell",
number = "4",

}

RIS

TY - JOUR

T1 - A new strategy for diagnostic model assessment in capture-recapture

AU - McCrea, Rachel

AU - Morgan, Byron J. T.

AU - Gimenez, Olivier

PY - 2017/7/8

Y1 - 2017/7/8

N2 - 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.

AB - 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.

KW - Goodness-of-fit tests

KW - Model selection

KW - Power

KW - Transience

KW - Trap dependence

KW - U-CARE

U2 - 10.1111/rssc.12197

DO - 10.1111/rssc.12197

M3 - Journal article

VL - 66

SP - 815

EP - 831

JO - Journal of the Royal Statistical Society: Series C (Applied Statistics)

JF - Journal of the Royal Statistical Society: Series C (Applied Statistics)

SN - 0035-9254

IS - 4

ER -