Conference paper
Efficient and robust estimation for longitudinal mixed models for binary data
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.
Language: | English |
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Year: | 2009 |
Proceedings: | International Symposium in Statistics (ISS) on Inferences in Generalized Linear Longitudinal Mixed Models (GLLMM) |
Types: | Conference paper |