Model Selection and Criticism

Abstract

In many applications of statistics, little prior knowledge or relevant theory is available, and so model choice becomes an entirely empirical, exploratory process. Three different approaches to model selection are described in the first three sections of this chapter. The first is a stepwise method, which starts from some initial model and successively adds or removes edges until some criterion is fulfilled. The second is a more global search technique proposed by Edwards and Havránek (1985, 1987), which seeks the simplest models consistent with the data. The third method is to select the model that optimizes one of the so-called information criteria (AIC or BIC). In Section 4 a brief comparison of the three approaches is made.