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Familial Models for Count Data

Chapter
Part of the Springer Series in Statistics book series (SSS)

Abstract

Familial models for count data are also known as Poisson mixed models for count data. In this setup, count responses along with a set of multidimensional covariates are collected from the members of a large number of independent families. Let y ij denote the count response for the jth (j =1, …,n i ) member on the ith (i=1, …, K) family/cluster. Also, let x ij = (x ij1, …,x ijp )′ denote the p covariates associated with the count response y ij . For example, in a health economics study, a state government may be interested to know the effects of certain socioeconomic and epidemiological covariates such as gender, education level, and age on the number of visits by a family member to the house physician in a particular year. Note that in this problem it is also likely that the count responses of the members of a family are influenced by a common random family effect, say γ i. This makes the count responses of any two members of the same family correlated, and this correlation is usually referred to as the familial correlation. It is of scientific interest to find the effects of the covariates on the count responses of an individual member after taking the familial correlations into account.

Keywords

Generalize Linear Mixed Model Relative Bias Count Response Familial Correlation Approximate Likelihood 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Science+Business Media, LLC 2011

Authors and Affiliations

  1. 1.Department of Mathematics and StatisticsMemorial UniversitySaint John’sCanada

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