Analysis of Counts involving Random Effects with Applications in Experimental Biology

Abstract This paper is concerned with the analysis of count data with special reference to experimental biology and agricultural research. The model considered in this paper is obtained by extending a generalized linear model by introducing random effects with associated variance components on the scale of the linear predictor. Maximum likelihood estimation is discussed and compared with a method which uses a simplified version of the likelihood equations. Two practical applications are used to illustrate the methods.

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