|Title||PERTURBATION AND SCALED COOK'S DISTANCE.|
|Publication Type||Journal Article|
|Year of Publication||2012|
|Authors||Zhu, Hongtu, Joseph G. Ibrahim, and Hyunsoon Cho|
Cook's (Cook, 1977) distance 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.
|Alternate Journal||Ann Stat|
|Original Publication||Perturbation and scaled Cook's distance.|
|PubMed Central ID||PMC3495605|
|Grant List||P01 ES014635 / ES / NIEHS NIH HHS / United States |
TL1 RR025745 / RR / NCRR NIH HHS / United States
R01 MH086633 / MH / NIMH NIH HHS / United States
P01 CA142538 / CA / NCI NIH HHS / United States
R01 CA074015 / CA / NCI NIH HHS / United States
PERTURBATION AND SCALED COOK'S DISTANCE.