Predictive Modeling Applications in Actuarial Science
 Volume 1
 Introduction
 Predictive Modeling Foundations
 Predictive Modeling Methods
 Bayesian and Mixed Modeling
 Longitudinal Modeling
 Volume 2
 Generalized Linear Model
 Extensions of the Generalized Linear Model
 Unsupervised Predictive Modeling Methods

Applications on Current Problems in Actuarial Science
 Chapter 8  The Predictive Distribution of Loss Reserve Estimates over a Finite Time Horizon
 Chapter 9  Finite Mixture Model and Workersâ€™ Compensation LargeLoss Regression Analysis
 Chapter 10  A Framework for Managing Claim Escalation Using Predictive Modeling
 Chapter 11  Predictive Modeling for UsageBased Auto Insurance
Chapter 19  Survival Models
Authors
Jim Robinson  University of Wisconsin  Madison
jim@chsra.wisc.edu
Chapter Preview
Survival modeling focuses on the estimation of failure time distributions from observed data. Failure time random variables are defined on the nonnegative 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 nonparametric 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, nonparametric and semiparametric models, isolate the impact of fixed and timevarying covariates, and analyze model residuals.
Data  R Code 
NNHS Data(RDA file)  NNHS Survival Modeling 