Predictive Modeling Applications in Actuarial Science

Chapter 19 - Survival Models


Jim Robinson | University of Wisconsin - Madison

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Survival modeling focuses on the estimation of failure time distributions from observed data. Failure time random variables are defined on the non-negative real numbers and might represent time to death, time to policy termination, or hospital length of stay. There are two defining aspects to survival modeling. First, it is not unusual to encounter distributions incorporating both parametric and non-parametric components, as will be seen with proportional hazard models. Second, the estimation techniques accommodate incomplete data, i.e., data which is only observed for a portion of the time exposed as a result of censoring or truncation.

In this chapter, we will apply R's survival modeling objects and methods to complete and incomplete data in order to estimate the distributional characteristics of the underlying failure time process. We will explore parametric, non-parametric and semi-parametric models, isolate the impact of fixed and time-varying covariates, and analyze model residuals.

Data R Code
NNHS Data(RDA file) NNHS Survival Modeling