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Examining Biliary Acid Constituents among Gall Bladder Patients: A Bayes Study Using the Generalized Linear Model
Pages 224-239
Puja Makkar, S.K. Upadhyay, V.K. Shukla and R.S. Singh
Published: 21 May 2015

Abstract: The generalized linear model is an important class of models that has wide variety of applications mainly because of its inherent flexibility and generality. The present paper provides an important application of GLM in order to examine different constituents of bile acid in the development of gallstones as well as carcinoma among the gallbladder patients. These constituents may be broadly categorized as primary and secondary bile acids. The paper, in fact, considers two particular cases of GLM based on normal and gamma modelling assumptions and provides the complete Bayes analysis using independent but vague priors for the concerned model parameters. It then analyzes a real data set taken from SS Hospital, Banaras Hindu University, with primary (secondary) bile acids as response variables and secondary (primary) bile acids as the predictors. The authenticity of the assumed models for the given data set is also examined based on predictive simulation ideas.

Keywords: Generalized linear model, vague priors, posterior distribution, biliary acids, gallbladder diseases, predictive simulation, Bayes information criterion.

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