Efficient and robust estimation for longitudinal mixed models for binary data

René Holst

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearch

Abstract

This paper proposes a longitudinal mixed model for binary data. The model extends the classical Poisson trick, in which a binomial regression is fitted by switching to a Poisson framework. A recent estimating equations method for generalized linear longitudinal mixed models, called GEEP, is used as a vehicle for fitting the conditional Poisson regressions, given a latent process of serial correlated Tweedie variables. The regression parameters are estimated using a quasi-score method, whereas the dispersion and correlation parameters are estimated by use of bias-corrected Pearson-type estimating equations, using second moments only. Random effects are predicted by BLUPs. The method provides a computationally efficient and robust approach to the estimation of longitudinal clustered binary data and accommodates linear and non-linear models. A simulation study is used for validation and finally the method is applied to some fishing gear selectivity data.
Original languageEnglish
Title of host publicationProceedings from the International Symposium in Statistics (ISS) on Inferences in Generalized Linear Longitudinal Mixed Models (GLLMM)
Publication date2009
Publication statusPublished - 2009
EventInternational Symposium in Statistics (ISS) on Inferences in Generalized Linear Longitudinal Mixed Models (GLLMM) - St. Johns, Canada
Duration: 1 Jan 2009 → …

Conference

ConferenceInternational Symposium in Statistics (ISS) on Inferences in Generalized Linear Longitudinal Mixed Models (GLLMM)
CitySt. Johns, Canada
Period01/01/2009 → …

Cite this

Holst, R. (2009). Efficient and robust estimation for longitudinal mixed models for binary data. In Proceedings from the International Symposium in Statistics (ISS) on Inferences in Generalized Linear Longitudinal Mixed Models (GLLMM)