Sensitivity of pearson's goodness-of-fit statistic in generalized linear models

This paper investigates the local influence assessment on the Pearson's goodness-of-fit statistic in generalized linear model settings. Inspired by Cook (1986), a local influence approach is adopted to assess model adequacy with respect to the contours of the unperturbed generalized Pearson's statistic. Based on local perturbations to the vectors of case weights, covariates and responses, the approach can detect different aspects of influence and yield additional insight to likelihood displacement. Two examples demonstrate the effectiveness of the proposed method. Copyright