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Docta Complutense author Martín Apaolaza, Níriam author Pardo Llorente, Leandro 2023-06-20T01:24:09Z 2023-06-20T01:24:09Z 2009-09-24 0266-4763 10.1080/02664760802562498 https://hdl.handle.net/20.500.14352/43572 https://doi.org/10.1080/02664760802562498 https://www.tandfonline.com/doi/full/10.1080/02664760802562498 It sometimes occurs that one or more components of the data exert a disproportionate influence on the model estimation. We need a reliable tool for identifying such troublesome cases in order to decide either eliminate from the sample, when the data collect was badly realized, or otherwise take care on the use of the model because the results could be affected by such components. Since a measure for detecting influential cases in linear regression setting was proposed by Cook [Detection of influential observations in linear regression, Technometrics 19 (1977), pp. 15–18.], apart from the same measure for other models, several new measures have been suggested as single-case diagnostics. For most of them some cutoff values have been recommended (see [D.A. Belsley, E. Kuh, and R.E. Welsch, Regression Diagnostics: Identifying Influential Data and Sources of Collinearity, 2nd ed., John Wiley & Sons, New York, Chichester, Brisban, (2004).], for instance), however the lack of a quantile type cutoff for Cook's statistics has induced the analyst to deal only with index plots as worthy diagnostic tools. Focussed on logistic regression, the aim of this paper is to provide the asymptotic distribution of Cook's distance in order to look for a meaningful cutoff point for detecting influential and leverage observations. eng Atribución-NoComercial 3.0 España On the Asymptotic Distribution of Cook’s distance in Logistic Regression Models journal article URL https://docta.ucm.es/bitstreams/02523f30-6a35-4a76-9900-b216502cb146/download File MD5 9d378ce6db1af62b44697170f18e8c49 274844 application/pdf leandro_pardo_asymptotic.pdf