Loss Models: From Data to Decisions, 4th Edition

1.2 Organization of this book

This text takes the reader through the modeling process, but not in the order presented in Section 1.1. There is a difference between how models are best applied and how they are best learned. In this text we first learn about the models and how to use them, and then we learn how to determine which model to use because it is difficult to select models in a vacuum. Unless the analyst has a thorough knowledge of the set of available models, it is difficult to narrow the choice to the ones worth considering. With that in mind, the organization of the text is as follows:

1. Review of probability—Almost by definition, contingent events imply probability models. Chapters 2 and 3 review random variables and some of the basic calculations that may be done with such models, including moments and percentiles.

2. Understanding probability distributions—When selecting a probability model, the analyst should possess a reasonably large collection of such models. In addition, in order to make a good a priori model choice, characteristics of these models should be available. In Chapters 4-7 various distributional models are introduced and their characteristics explored. This includes both continuous and discrete distributions.

3. Coverage modifications—Insurance contracts often do not provide full payment. For example, there may be a deductible (e.g., the insurance policy does not pay the first $250) or a limit (e.g., the insurance policy does not pay more than ...