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 Workers’ Compensation Large-Loss Regression Analysis
- Chapter 10 - A Framework for Managing Claim Escalation Using Predictive Modeling
- Chapter 11 - Predictive Modeling for Usage-Based 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 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 |