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 2 - Overview of Linear Models
Authors
Margie Rosenberg | University of Wisconsin - Madison
mrosenberg@bus.wisc.edu
Jim Gusczca | Deloitte
jguszcza@deloitte.com
Chapter Preview
Linear modeling, also known as regression analysis, is a core tool in statistical practice for data analysis, prediction and decision support. Applied data analysis requires judgment, domain knowledge, and the ability to analyze data.
This chapter provides a summary of the linear model, discusses model assumptions, parameter estimation, variable selection and model validation around a series of examples. These examples are grounded in data to help relate the theory to practice. All of these practical examples and exercises are completed using the open-source R statistical computing package. Particular attention is paid to the role of Exploratory Data Analysis in the iterative process of criticizing, improving, and validating models in a detailed case study. Linear models provide a foundation for many of the more advanced statistical and machine learning techniques that are explored in the later chapters of this volume.
| Data | R Code |
| MEPS Diabetes Data | Examples and Case Study Code |
| Exercise Code |