FIRM: Formal Inference-based Recursive Modeling

Recursive modeling is an attractive data-analytic tool for studying the relationship between a dependent variable and a collection of predictor variables. In this, it complements methods such as multiple regression, analysis of variance, neural nets, generalized additive models, discriminant analysis and log linear modeling. It is particularly helpful when simple models like regression do not work, as happens when the relationship connecting the variables is not straightforward—for example if there are synergies between the predictors. It is attractive when there is missing information in the data. It can also be valuable when used in conjunction with a more traditional statistical data analysis, as it may provide evidence that the model assumptions underlying the traditional analysis are valid, something that is otherwise not easy to do.