Why is it so hard to get consensus on a good variable selection technique for exploratory models? The classic one, implemented everywhere and understood by everyone, is really sub-optimal (Stepwise regression -- I am looking at you). It seems to just love to include noise parameters which make any and all models difficult to explain.
Sure, you can build a model based on theory but what do you do when you want to know what factors might be associated with an outcome? And, of course, exploratory datasets tend to be the smallest possible data sets!!
Grrrr . . . .