On familial longitudinal Poisson mixed models with gamma random effects

Brajendra C. Sutradhar and Vandna Jowaheer

Journal of Multivariate Analysis, 2003, vol. 87, issue 2, 398-412

Abstract: Poisson mixed models are used to analyze a wide variety of cluster count data. These models are commonly developed based on the assumption that the random effects have either the log-normal or the gamma distribution. Obtaining consistent as well as efficient estimates for the parameters involved in such Poisson mixed models has, however, proven to be difficult. Further problem gets mounted when the data are collected repeatedly from the individuals of the same cluster or family. In this paper, we introduce a generalized quasilikelihood approach to analyze the repeated familial data based on the familial structure caused by gamma random effects. This approach provides estimates of the regression parameters and the variance component of the random effects after taking the longitudinal correlations of the data into account. The estimators are consistent as well as highly efficient.

Keywords: Multi-dimensional; count; data; Mixed; effects; Quasilikelihood; Efficiency; Repeated; count; responses; Regression; effects; Variance; component; of; the; random; effects; Longitudinal; correlations (search for similar items in EconPapers)
Date: 2003
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Citations: View citations in EconPapers (3)

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