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---
title: "Loss Models: From Data to Decisions, Fifth Edition"
tags:
- authorship/other
- exclude-from-word-count
- topic/risk
- type/media/book
authors:
- Gordon E. Willmot
- Harry H. Panjer
- Stuart A. Klugman
edition: Fifth
publisher: John Wiley & Sons, Inc.
series: Wiley Series in Probability and Statistics
subtitle: From Data to Decisions
type: book
year: 2019
---
# Loss Models: From Data to Decisions, Fifth Edition
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This note, with the exception of comments like this one
(reserved for notes on transcription)
consists only of content from the text.
For commentary see the companion [[loss-models]].
%%
## Part I Introduction
### 1. Modeling
#### 1.1 The Model-Based Approach
##### 1.1.1 The Modeling Process
The model-based approach
should be considered in the context of the objectives of any given problem.
Many problems in actuarial science
involve the building of a mathematical model
that can be used to forecast or predict insurance costs in the future.
##### 1.1.2 The Modeling Advantage
#### 1.2 The Organization of This Book
### 2. Random Variables
#### 2.1 Introduction
#### 2.2 Key Functions and Four Models
##### 2.2.1 Exercises
### 3. Basic Distributional Quantities
#### 3.1 Moments
##### 3.1.1 Exercises
#### 3.2 Percentiles
##### 3.2.1 Exercises
#### 3.3 Generating Functions and Sums of Random Variables
##### 3.3.1 Exercises
#### 3.4 Tails of Distributions
##### 3.4.1 Classification Based on Moments
##### 3.4.2 Comparison Based on Limiting Tail
##### 3.4.3 Classification Based on the Hazard Rate Function
##### 3.4.4 Classification Based on the Mean Excess Loss Function The mean excess
##### 3.4.5 Equilibrium Distributions and Tail Behavior
##### 3.4.6 Exercises
#### 3.5 Measures of Risk
##### 3.5.1 Introduction
##### 3.5.2 Risk Measures and Coherence
##### 3.5.3 Value at Risk
##### 3.5.4 Tail Value of Risk
##### 3.5.5 Exercises
## Part II Actuarial Models
### 4. Characteristics Of Actuarial Models
#### 4.1 Introduction
#### 4.2 The Role of Parameters
##### 4.2.1 Parametric and Scale Distributions
##### 4.2.2 Parametric Distribution Families
##### 4.2.3 Finite Mixture Distributions
##### 4.2.4 Data-Dependent Distributions
##### 4.2.5 Exercises
### 5. Continuous Models
#### 5.1 Introduction
#### 5.2 Creating New Distributions
##### 5.2.1 Multiplication by a Constant
##### 5.2.2 Raising to a Power
##### 5.2.3 Exponentiation
##### 5.2.4 Mixing
##### 5.2.5 Frailty Models
##### 5.2.6 Splicing
##### 5.2.7 Exercises
#### 5.3 Selected Distributions and Their Relationships
##### 5.3.1 Introduction
##### 5.3.2 Two Parametric Families
##### 5.3.3 Limiting Distributions
##### 5.3.4 Two Heavy-Tailed Distributions
##### 5.3.5 Exercises
#### 5.4 The Linear Exponential Family
##### 5.4.1 Exercises
### 6. Discrete Distributions
#### 6.1 Introduction
##### 6.1.1 Exercise
#### 6.2 The Poisson Distribution
#### 6.3 The Negative Binomial Distribution
#### 6.4 The Binomial Distribution
#### 6.5 The (a,b) Class
##### 6.5.1 Exercises
#### 6.6 Truncation and Modification at zero
##### 6.6.1 Exercises
### 7. Advanced Discrete Distributions
#### 7.1 Compound Frequency Distributions
##### 7.1.1 Exercises
#### 7.2 Further Properties of the Compound Poisson Class
##### 7.2.1 Exercises
#### 7.3 Mixed-Frequency Distributions
##### 7.3.1 The General Mixed-Frequency Distribution
##### 7.3.2 Mixed Poisson Distributions
##### 7.3.3 Exercises
#### 7.4 The Effect of Exposure on Frequency
#### 7.5 An Inventory of Discrete Distributions
##### 7.5.1 Exercises
### 8. Frequency And Severity With Coverage Modifications
#### 8.1 Introduction
#### 8.2 Deductibles
##### 8.2.1 Exercises
#### 8.3 The Loss Elimination Ratio and the Effect of Inflation for Ordinary Deductibles
##### 8.3.1 Exercise
#### 8.4 Policy Limits
##### 8.4.1 Exercises
#### 8.5 Coinsurance, Deductibles, and Limits
##### 8.5.1 Exercises
#### 8.6 The Impact of Deductibles on Claim Frequency
##### 8.6.1 Exercises
### 9. Aggregate Loss Models
#### 9.1 Introduction
##### 9.1.1 Exercises
#### 9.2 Model Choices
##### 9.2.1 Exercises
#### 9.3 The Compound Model for Aggregate Claims
##### 9.3.1 Probabilities and Moments
##### 9.3.2 Stop-Loss Insurance
##### 9.3.3 The Tweedle Distribution
##### 9.3.4 Exercises
#### 9.4 Analytic Results
##### 9.4.1 Exercises
#### 9.5 Computing the Aggregate Claims Distribution
#### 9.6 The Recursive Method
##### 9.6.1 Applications to Compound Frequency Models
##### 9.6.2 Underflow/Overflow Problems
##### 9.6.3 Numerical Stability
##### 9.6.4 Continuous Severity
##### 9.6.5 Constructing Arithmetic Distributions
##### 9.6.6 Exercises
#### 9.7 The Impact of Individual Policy Modifications on Aggregate Payments
##### 9.7.1 Exercises
#### 9.8 The individual Risk Model
##### 9.8.1 The Model
##### 9.8.2 Parametric Approximation
##### 9.8.3 Compound Poisson Approximation
##### 9.8.4 Exercises
## Part III Mathematical Statistics
### 10. Introduction To Mathematical Statistics
#### 10.1 Introduction and Four Data Sets
#### 10.2 Point Estimation
##### 10.2.1 Introduction
##### 10.2.2 Measures of Quality
##### 10.2.3 Exercises
#### 10.3 Interval Estimation
##### 10.3.1 Exercises
#### 10.4 The Construction of Parametric Estimators
##### 10.4.1 The Method of Moments and Percentile Matching
##### 10.4.2 Exercises
#### 10.5 Tests of Hypotheses
##### 10.5.1 Exercise
### 11. Maximum Likelihood Estimation
#### 11.1 Introduction
#### 11.2 Individual Data
##### 11.2.1 Exercises
#### 11.3 Grouped Data
##### 11.3.1 Exercises
#### 11.4 Truncated or Censored Data
##### 11.4.1 Exercises
#### 11.5 Variance and Interval Estimation for Maximum Likelihood Estimators
##### 11.5.1 Exercises
#### 11.6 Functions of Asymptotically Normal Estimators
##### 11.6.1 Exercises
#### 11.7 Nonnormal Confidence Intervals
##### 11.7.1 Exercise
### 12. Frequentist Estimation For Discrete Distributions
#### 12.1 The Poisson Distribution
#### 12.2 The Negative Binomial Distribution
#### 12.3 The Binomial Distribution
#### 12.4 The (a,b) Class
#### 12.5 Compound Models
#### 12.6 The Effect of Exposure on Maximum Likelihood Estimation
#### 12.7 Exercises
### 13. Bayesian Estimation
#### 13.1 Definitions and Bayes' Theorem
#### 13.2 Inference and Prediction
##### 13.2.1 Exercises
#### 13.3 Conjugate Prior Distributions and the Linear Exponential Family
##### 13.3.1 Exercises
#### 13.4 Computational Issues
## Part IV Construction Of Models
### 14. Construction Of Empirical Models
#### 14.1 The Empirical Distribution
#### 14.2 Empirical Distributions for Grouped Data
##### 14.2.1 Exercises
#### 14.3 Empirical Estimation with Right Censored Data
##### 14.3.1 Exercises
#### 14.4 Empirical Estimation of Moments
##### 14.4.1 Exercises
#### 14.5 Empirical Estimation with Left Truncated Data
##### 14.5.1 Exercises
#### 14.6 Kernel Density Models
##### 14.6.1 Exercises
#### 14.7 Approximations for large Data Sets
##### 14.7.1 Introduction
##### 14.7.2 Using Individual Data Points
##### 14.7.3 Interval-Based Methods
##### 14.7.4 Exercises
#### 14.8 Maximum Likelihood Estimation of Decrement Probabilities
##### 14.8.1 Exercise
#### 14.9 Estimation of Transition Intensities
### 15. Model Selection
#### 15.1 Introduction
#### 15.2 Representations of the Data and Model
#### 15.3 Graphical Comparison of the Density and Distribution Functions
##### 15.3.1 Exercises
#### 15.4 Hypothesis Tests
##### 15.4.1 The Kolmogorov--Smirnov Test
##### 15.4.2 The Anderson--Darling Test
##### 15.4.3 The Chi-Square Goodness-of-Fit Test
##### 15.4.4 The Likelihood Ratio Test
##### 15.4.5 Exercises
#### 15.5 Selecting a Model
##### 15.5.1 Introduction
##### 15.2.2 Judgement-Based Approaches
##### 15.5.3 Score-Based Approaches
##### 15.5.4 Exercises
## Part V Credibility
### 16. Introduction To Limited Fluctuation Credibility
#### 16.1 Introduction
#### 16.2 Limited Fluctuation Credibility Theory
#### 16.3 Full Credibility
#### 16.4 Partial Credibility
#### 16.5 Problems with the Approach
#### 16.6 Notes and References
#### 16.7 Exercises
### 17. Greatest Accuracy Credibility
#### 17.1 Introduction
#### 17.2 Conditional Distributions and Expectation
#### 17.3 The Bayesian Methodology
#### 17.4 The Credibility Premium
#### 17.5 The Bühlmann Model
#### 17.6 The Bühlmann--Straub Model
#### 17.7 Exact Credibility
#### 17.8 Notes and References
#### 17.9 Exercises
### 18. Empirical Bayes Parameter Estimation
#### 18.1 Introduction
#### 18.2 Nonparametric Estimation
#### 18.3 Semiparametric Estimation
#### 18.4 Notes and References
#### 18.5 Exercises
## Part VI Simulation
### 19. Simulation
#### 19.1 Basics of Simulation
##### 19.1.1 The Simulation Approach
##### 19.1.2 Exercises
#### 19.2 Simulation for Specific Distributions
##### 19.2.1 Discrete Mixtures
##### 19.2.2 Time or Age of Death from a Life Table
##### 19.2.3 Simulating from the (a,b) Class
##### 19.2.4 Normal and Lognormal Distributions
##### 19.2.5 Exercises
#### 19.3 Determining the Sample Size
##### 19.3.1 Exercises
#### 19.4 Examples of Simulation in Actuarial Modeling
##### 19.4.1 Aggregate Loss Calculations
##### 19.4.2 Examples of Lack of Independence
##### 19.4.3 Simulation Analysis of the Two Examples
##### 19.4.4 The Use of Simulation to Determine Risk Measures
##### 19.4.5 Statistical Analyses
##### 19.4.6 Exercises
## Appendix A An Inventory Of Continuous Distributions
### A.1 Introduction
### A.2 The Transformed Beta Family
#### A.2.1 The Four-Parameter Distribution
#### A.2.2 Three-Parameter Distributions
#### A.2.3 Two-Parameter Distributions
### A.3 The Transformed Gamma Family
#### A.3.1 Three-Parameter Distributions
#### A.3.2 Two-Parameter Distributions
#### A.3.3 One-Parameter Distributions
### A.4 Distributions For Large Losses
#### A.4.1 Extreme Value Distributions
#### A.4.2 Generalized Pareto Distributions
### A.4.5 Other Distributions
### A.4.6 Distributions With Finite Support
## Appendix B An Inventory Of Discrete Distributions
### B.1 Introduction
### B.2 The (a,b,0) Class
### B.3 The (a,b,1) Class
#### B.3.1 The Zero-Truncated Subclass
#### B.3.2 The Zero-Modified Subclass
### B.4 The Compound Class
#### B.4.1 Some Compound Distributions
### B.5 A Hierarchy Of Discrete Distributions
## Appendix C Frequency And Severity Relationships
## Appendix D The Recursive Formula
## Appendix E Discretization Of The Severity Distribution
### E.1 The Method Of Rounding
### E.2 Mean Preserving
### E.3 Undiscretization Of A Discretized Distribution
## References
## Index