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A conditional count model for repeated count data and its application to GEE approach. (English) Zbl 1371.62059
Summary: In this article, a conditional model is proposed for modeling longitudinal count data. The joint density is disintegrated into the marginal and conditional densities according to the multiplication rule. It allows both positive and negative correlation among variables, which most multivariate count models do not possess. To show the efficiency of the proposed model for count data, we have applied to the generalized estimating equations and the inverse Fisher information matrix is compared with the covariance matrix from estimating equations. A simulation experiment is displayed and an application of the model to divorce data is presented. In addition, a comparison of conditional model and bivariate Poisson model proposed by S. Kocherlakota and K. Kocherlakota [Bivariate discrete distributions. New York, NY: Marcel Dekker (1992; Zbl 0794.62002)] has shown using simulated data.

MSC:
62J12 Generalized linear models (logistic models)
62H05 Characterization and structure theory for multivariate probability distributions; copulas
62E10 Characterization and structure theory of statistical distributions
Software:
gcmr; weightedScores
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