This talk builds upon the Invisible Fence (Jiang et al., 2011) a promising model selection method. Utilizing a combination of coeffcient, scale and deviance estimates we are able to improve this resampling based model selection method for regression models, both linear and general linear models. The introduction of a variable inclusion plot allows for a visual representation for the stability of the model selection method as well as the variables bootstrapped rank. The suggested methods will be applied to both simulated and real examples with comparisons about both computational time and effectiveness made to selections through alternative selection procedures. We will report on our latest results from ongoing work in scaling up subtractive stability measures when the numbers offeatures is large.

References:

Jiang, J., Nguyen, T., & Rao, J. S. (2011). Invisible fence methods and the identication of differentially expressed gene sets. Statistics and its Interface, 4(3), 403-415.