Influence analysis of additive mixed-effects nonlinear regression models via EM algorithm

Abstract

This paper presents a unified method for influence analysis to deal with random effects appeared in additive nonlinear regression models for repeated measurement data. The basic idea is to apply the Q-function, the conditional expectation of the complete-data log-likelihood function obtained from EM algorithm, instead of the observed-data log-likelihood function as used in standard influence analysis. Diagnostic measures are derived based on the case-deletion approach and the local influence approach. Two real examples and a simulation study are examined to illustrate our methodology.

Keywords:

• EM algorithm
• case-deletion
• local influence
• mixed-effects nonlinear regression
• Q-displacement function

Acknowledgements

We would like to thank Associate Editor and referees for their helpful comments and suggestions that led to a significant improvement of the paper. This work is supported by NSFC (10671032) and NSFJS (BK2008284).