Skip to main content

Two ways of modelling overdispersion in non-normal data

Buy Article:

$51.00 plus tax (Refund Policy)

Abstract:

For non-normal data assumed to have distributions, such as the Poisson distribution, which have an a priori dispersion parameter, there are two ways of modelling overdispersion: by a quasi-likelihood approach or with a random-effect model. The two approaches yield different variance functions for the response, which may be distinguishable if adequate data are available. The epilepsy data of Thall and Vail and the fabric data of Bissell are used to exemplify the ideas.

Keywords: Hierarchical generalized linear models; Model checking; Overdispersion; Quasi-likelihood model; Random-effect model; Robust residual

Document Type: Original Article

DOI: http://dx.doi.org/10.1111/1467-9876.00214

Affiliations: 1: Seoul National University, Korea, 2: Imperial College of Science, Technology and Medicine, London, UK

Publication date: January 1, 2000

bpl/rssc/2000/00000049/00000004/art00012
dcterms_title,dcterms_description,pub_keyword
6
5
20
40
5

Access Key

Free Content
Free content
New Content
New content
Open Access Content
Open access content
Subscribed Content
Subscribed content
Free Trial Content
Free trial content
Cookie Policy
X
Cookie Policy
Ingenta Connect website makes use of cookies so as to keep track of data that you have filled in. I am Happy with this Find out more