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Influence diagnostics for generalized CP tensor regression models
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DOI：10.1080/00949655.2025.2554295.png)
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Abstract
En 中文
Tensor regression models are widely used in diverse fields, but influence diagnostics for these models remain underdeveloped. This study extends local influence analysis and the case-deletion method to generalized CP tensor regression. We derive one-step approximations of generalized Cook's distance using the Hessian and Fisher information matrices. Three perturbation schemes-case-weighted, single-explanatory-variable, and group-explanatory-variable-are analyzed via the likelihood displacement's largest curvature. Simulations and empirical results confirm that our diagnostic methods accurately identify influential observations, even under higher-than-true rank assumptions.
Keywords:
Case-deletion method
diagnostic statistics
generalized CP tensor regression model
local influence analysis
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