reading classics (#2)

Following last week read of Hartigan and Wong’s 1979 K-Means Clustering Algorithm, my Master students in the Reading Classics Seminar course, listened today to Agnė Ulčinaitė covering Rob Tibshirani‘s original LASSO paper Regression shrinkage and selection via the lasso in JRSS Series B. Here are her (Beamer) slides

Again not the easiest paper in the list, again mostly algorithmic and requiring some background on how it impacted the field. Even though Agnė also went through the Elements of Statistical Learning by Hastie, Friedman and Tibshirani, it was hard to get away from the paper to analyse more widely the importance of the paper, the connection with the Bayesian (linear) literature of the 70′s, its algorithmic and inferential aspects, like the computational cost, and the recent extensions like Bayesian LASSO. Or the issue of handling n<p models. Remember that one of the S in LASSO stands for shrinkage: it was quite pleasant to hear again about ridge estimators and Stein’s unbiased estimator of the risk, as those were themes of my Ph.D. thesis… (I hope the students do not get discouraged by the complexity of those papers: there were fewer questions and fewer students this time. Next week, the compass will move to the Bayesian pole with a talk on Lindley and Smith’s 1973 linear Bayes paper by one of my PhD students.)

One Response to “reading classics (#2)”

  1. One of the interesting but, at the same time, *very disturbing* properties of the Lasso is that it sets some coefficients to 0, contrary to Ridge (or BLUP, or Stein, or whatever one wants to call it).

    This gives a false sensation of “knowledge” (“this thing has no effect”) whereas in fact Lasso is telling “hey, this set of predictors is good enough given the restriction t that *you* imposed on them”.

    But if effects are collinear (as often happens in genetics and possibly other fields) then change a little the data and Lasso will pick another set of predictors.

    I wonder if I am the only one as feeling this trouble of interpretation as a problem :-/

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