Research Description
Abstract The identification of influential observations is an essential element in regression analysis
as they posed a threat to the model building process. The existence of multicollinearity among the
regressors complicates the presence of influential observations. Different influential diagnostics
have been presented in literature so far using generalized linear models (GLM). In this paper,
approximate deletion measures based on Sherman–Morrison Woodbury (SMW) theorem for the
Poisson Two-Parameter regression model are proposed to detect influential observations in the
presence of multicollinearity. Moreover, we conduct a Monte Carlo Simulation to evaluate the performance of the proposed measures. Finally, an example is presented to illustrate the proposed
diagnostic measures.
