Loss Models: From Data to Decisions, 4th Edition

16.1 Introduction

When using data to build a model, the process must end with the announcement of a “winner,” While qualifications, limitations, caveats, and other attempts to escape full responsibility are appropriate, and often necessary, a commitment to a solution is often required. In this chapter we look at a variety of ways to evaluate a model and compare competing models. But we must also remember that whatever model we select, it is only an approximation of reality. This observation is reflected in the following modeler’s motto:

All models are wrong, but some models are useful.¹

Thus, our goal is to determine a model that is good enough to use to answer the question. The challenge here is that the definition of “good enough” will depend on the particular application. Another important modeling point is that a solid understanding of the question will guide you to the answer. The following quote from John Tukey [112, pp. 13–14] sums up this point:

Far better an approximate answer to the right question, which is often vague, than an exact answer to the wrong question, which can always be made precise.

In this chapter, a specific modeling strategy is considered. Our preference is to have a single approach that can be used for any probabilistic modeling situation. A consequence is that for any particular modeling situation there may be a better (more reliable or more accurate) approach. For example, while maximum likelihood is a good estimation method for most settings, it may ...