### 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 language | English |
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Title of host publication | Proceedings from the International Symposium in Statistics (ISS) on Inferences in Generalized Linear Longitudinal Mixed Models (GLLMM) |

Publication date | 2009 |

Publication status | Published - 2009 |

Event | International Symposium in Statistics (ISS) on Inferences in Generalized Linear Longitudinal Mixed Models (GLLMM) - St. Johns, Canada Duration: 1 Jan 2009 → … |

### Conference

Conference | International Symposium in Statistics (ISS) on Inferences in Generalized Linear Longitudinal Mixed Models (GLLMM) |
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City | St. Johns, Canada |

Period | 01/01/2009 → … |

## 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)*