Large numbers of explanatory variables, a semi-descriptive analysis

Significance Data with a small number of study individuals and a large number of potential explanatory features arise particularly in genomics. Existing methods of analysis result in a single model, but other sparse choices of explanatory features may fit virtually equally well. Our primary aim is essentially a set of acceptable simple representations. The method allows the assessment of anomalies, such as nonlinearities and interactions.