**F**ollowing 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

**A**gain 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.)