Abstract
We explore the possibility of modeling clustered count data using the Poisson Inverse Gaussian distribution. We develop a regression model, which relates the number of mastitis cases in a sample of dairy farms in Ontario, Canada, to various farm level covariates, to illustrate the method ology. Residual plots are constructed to explore the quality of the fit. We compare the results with a negative binomial regression model using max imum likelihood estimation, and to the generalized linear mixed regression model fitted in SAS.
Highlights
Mastitis may be defined as inflammation of the mammary gland of dairy cows, and may be categorized into subclinical and clinical mastitis
Identification of udders infected with sub-clinical mastitis may be performed with bacteriology and California Mastitis Test (CMT)
The most common CS approaches are Generalized Linear Mixed Models (GLMMS), which extend the class of generalized linear models by including random effects in the linear predictor
Summary
Mastitis may be defined as inflammation of the mammary gland of dairy cows, and may be categorized into subclinical and clinical mastitis. Cows with contagious mastitis are usually culled to prevent the spread of the disease to other healthy cows in the herd. Due to these losses, the disease is of considerable economic importance. Composite samples were taken from each milking cow in the herd, and bacteriology was performed at the Animal Health Laboratory, Ontario Veterinary College, at the University of Guelph, to identify the pathogen causing sub-clinical mastitis. The most common CS approaches are Generalized Linear Mixed Models (GLMMS), which extend the class of generalized linear models by including random effects in the linear predictor
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