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Perturbation and scaled Cook’s distance. (English) Zbl 1273.62180

Summary: R. D. Cook’s distance [Technometrics 19, 15–18 (1977; Zbl 0371.62096)] is one of the most important diagnostic tools for detecting influential individual or subsets of observations in linear regression for cross-sectional data. However, for many complex data structures (e.g., longitudinal data), no rigorous approach has been developed to address a fundamental issue: deleting subsets with different numbers of observations introduces different degrees of perturbation to the current model fitted to the data, and the magnitude of Cook’s distance is associated with the degree of the perturbation. The aim of this paper is to address this issue in general parametric models with complex data structures. We propose a new quantity for measuring the degree of the perturbation introduced by deleting a subset. We use stochastic ordering to quantify the stochastic relationship between the degree of the perturbation and the magnitude of Cook’s distance. We develop several scaled Cook’s distances to resolve the comparison of Cook’s distance for different subset deletions. Theoretical and numerical examples are examined to highlight the broad spectrum of applications of these scaled Cook’s distances in a formal influence analysis.

MSC:

62J20 Diagnostics, and linear inference and regression
62J12 Generalized linear models (logistic models)

Citations:

Zbl 0371.62096
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