 Chi‐Square Tests for Overdispersion with Multiparameter EstimatesJournal of the Royal Statistical Society Series C, 1989, vol. 38, issue 3, 441-453 Abstract: When a vector of sample proportions is not obtained through simple random sampling, the covariance matrix for the sample vector can differ substantially from that corresponding to the multinomial model. For example, clustering effects or subject effects in repeated measure experiments can cause the variance of the observed proportions to be much larger than variances under the multinomial model. The phenomenon is generally referred to as overdispersion. This paper presents a method of analysing categorical data in the presence of overdispersion. The actual distribution of the sample proportion need not be known but it is assumed that the covariance matrix is a function of the population proportions and a limited number of scaling parameters. Parameter estimates are obtained through a combination of generalized least squares and moment estimation techniques and large sample chi‐square tests are developed. Tests of fit of the models proposed are also developed and illustrated. Comparisons are made with other methods proposed in the literature, for overdispersed data. Date: 1989 Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:bla:jorssc:v:38:y:1989:i:3:p:441-453 Ordering information: This journal article can be ordered from Access Statistics for this article Journal of the Royal Statistical Society Series C is currently edited by R. Chandler and P. W. F. Smith More articles in Journal of the Royal Statistical Society Series C from Royal Statistical Society Contact information at EDIRC. |
Is your work missing from RePEc? Here is how to contribute.
Questions or problems? Check the EconPapers FAQ or send mail to .
EconPapers is hosted by the
School of Business at Örebro University.