Murphy Choy

Stepwise selection in regression? A poor choice

In Uncategorized on June 21, 2011 at 11:00 am

Stepwise selection is almost the most common model selection technique in credit score building. It is also by far the most common approach taught in college for model building. However, this technique is not flawless and has serious drawbacks which demand a closer examination from the analysts.

The classic model selection techniques are all compromised by a flawed comparison process which is akin to the problem faced in multiple comparison tests. Now without me getting into all sort of tough stuffs on this light hearted blog. I will suggest everyone to pay a visit to one of the SAS papers online which detailed much of the atrocities of the selection technique.

Have a look at David and Peter’s paper.

Another random rant from a SASer!

  1. Interesting that you say it is the most common approach taught in college for model building. I don’ t have any data to suggest otherwise, but that is a depressing thought, if true. I touch on stepwise regression when I teach statistics but I primarily focus on the drawbacks and why it is not a good idea.

    • Well, I went through a lot of courses currently taught by existing faculty on applied regression analysis and almost every course stopped at Stepwise selection regression as the last model that they teach. Given that most students will not move on to advance regression modeling, this creates the false impression that stepwise selection regression is the most important model for them to use. I am very unfortunate to have a lecturer who taught me regression which ended up with LASSO as the last model selection technique although it was far too advanced for most of us at that point of time.

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