Influence functions for proportional hazards regression

Abstract

Influence functions for the regression parameters in the proportional hazards model are presented. It is suggested that empirical influence functions, computed for each observation and each covariate, can be useful in an informal way to identify influential observations. This is illustrated on the Stanford heart transplant data and two other examples.