Journal of Advances in Applied Mathematics

Download PDF (589.9 KB) PP. 149 - 159 Pub. Date: July 12, 2016

DOI: 10.22606/jaam.2016.13002

• Harmanpreet Singh Kapoor
  Department of Statistics, Panjab University, Chandigarh
• Kanchan Jain^*
  Department of Statistics, Panjab University, Chandigarh
• Suresh Kumar Sharma
  Department of Statistics, Panjab University, Chandigarh

The widely used Generalized Additive Model (GAM) is a flexible and effective technique for conducting non-linear regression analysis. It relaxes the usual parametric assumptions and enables us to uncover structure in the relationship between the independent and dependent variable in exponential family that might be otherwise missed. In this paper, we describe the use of GAM procedure to determine the premium amount of diabetic patients in presence of predictors or covariates. The risk factors responsible for the cause of the diabetic patients have also been identified using Logistic GAM. The procedure has been applied to a real life data set of 134 diabetic patients by smoothening the effect of covariates.

Generalized additive model, Smoothing, Premium, Generalized cross validation, Splines.

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