Archive for Master program

reading classics (#2)

Posted in Statistics, University life with tags , , , , , , , , , , , on November 8, 2012 by xi'an

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.)

reading classics (#1)

Posted in Statistics, University life with tags , , , , , , , on October 26, 2012 by xi'an

This year, a lot of my Master students (plus all of my PhD students) registered for the Reading Classics Seminar course, so we should spend half of the year going through those “classics“. And have lively discussions thanks to the size of the group. The first student to present a paper, Céline Beji, chose Hartigan and Wong’s 1979 K-Means Clustering Algorithm paper in JRSS C. She did quite well, esp. when considering she had two weeks to learn \mathrm{L\!\!^{{}_{\scriptstyle A}} \!\!\!\!\!\;\; T\!_{\displaystyle E} \! X} and Beamer in addition to getting thru the paper! She also managed to find an online demo of the algorithm. Here are her slides

This was not the easiest paper in the list, by far: it is short, mostly algorithmic and somehow requires some background on the reasons why clustering was of interest and on how it impacted the field. Tellingly, the discussion with the class then focussed on the criterion rather than on the algorithm itself. In a sense, this is the most striking feature of the paper, namely that it is completely a-statistical in picking a criterion to minimise. there is neither randomness nor error involved at this stage, it is simply an extended least-square approach. This is why the number of clusters—and again the discussion from the class spent some time on this—cannot be inferred via this method. A well-auguring start to the course!