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Activity Number:
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194
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Type:
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Contributed
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Date/Time:
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Monday, August 1, 2016 : 10:30 AM to 12:20 PM
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Sponsor:
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Section on Nonparametric Statistics
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Abstract #320271
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Title:
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Bayesian Approach to Survival Models for Events of Insurance Claims
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Author(s):
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Prashant KC* and Deepak Sanjel
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Companies:
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Minnesota State University Mankato and Minnesota State University Mankato
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Keywords:
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Survival Analysis ;
Cox proportional hazard model ;
Bayesian method ;
Markov Chain Monte Carlo ;
Survival Models
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Abstract:
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Survival Analysis is heavily used in medical research to study the rate of death or survival probability in patients after they have received treatments or organ transplants. Survival model can also be very useful in analyzing insurance claim data to calculate the risk of claims and to determine the insurance premiums, in life or auto insurance. Applications of some of the commonly used survival methods such as Kaplan-Meier estimator, and the CoxPH model will be presented in connection with actuarial application. The results will be compared with Bayesian approach using the Markov Chain Monte Carlo (MCMC) method.
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Authors who are presenting talks have a * after their name.
