Nonlinear regression models for correlated count data

✍ Scribed by R. T. Burnett; J. Shedden; D. Krewski

• Publisher: John Wiley and Sons
• Year: 1992
• Tongue: English
• Weight: 639 KB
• Volume: 3
• Category: Article
• ISSN: 1180-4009
• DOI: 10.1002/env.3170030206

✦ Synopsis

Abstract

In this article, nonlinear regression models for correlated count data are examined. Correlation within clusters is modelled by a multivariate Gaussian mixing process on the log‐expectation scale. The regression parameters and the variance‐covariance parameters of the mixing process are estimated using quasi‐likelihood methods. An example involving temporal trends in hospital admissions for respiratory disease is used to illustrate the methods proposed.