COVARIATE TRANSFORMATION DIAGNOSTICS FOR GENERALIZED LINEAR MODELS

ANDY H. LEE AND JOHN S. YICK

Faculty of Science, Northern Territory University, Darwin, NT 0909, Australia

(Received April 30, 1996; revised October 20, 1997)

Abstract. Transformations of covariates are commonly applied in regression analysis. When a parametric transformation family is used, the maximum likelihood estimate of the transformation parameter is often sensitive to minor perturbations of the data. Diagnostics are derived to assess the influence of observations on the covariate transformation parameter in generalized linear models. Three numerical examples are presented to illustrate the usefulness of the proposed diagnostics.

Key words and phrases: Local influence, partial influence, profile likelihood displacement, transformation diagnostics.