@article{ref1,
title="Assessing influence for pharmaceutical data in zero‐inflated generalized Poisson mixed models",
journal="Statistics in Medicine",
year="2008",
author="Xie, Feng‐Chang and Wei, Bo‐Cheng and Lin, Jin‐Guan",
volume="27",
number="18",
pages="3656-3673",
abstract="For clustered count data with excess zeros where the observations are either over-dispersed or under-dispersed, the zero-inflated generalized Poisson mixed (ZIGPM) regression model may be appropriate, in which the baseline discrete distribution is a generalized Poisson distribution, which is a natural extension of standard Poisson distribution. Motivated by one data set drawn from a pharmaceutical study, influence diagnostics for ZIGPM models based on case-deletion and local influence analysis are developed in this work. The one-step approximations of the estimates under case-deletion model and some case-deletion measures are given. Meanwhile, local influence measures are obtained under various perturbations of the observed data or model assumptions. Results from a pharmaceutical study illustrate the usefulness of the diagnostic statistics. Copyright © 2008 John Wiley & Sons, Ltd.<p />",
language="",
issn="0277-6715",
doi="10.1002/sim.3233",
url="http://dx.doi.org/10.1002/sim.3233"
}