# The Tibshirani’s – Statistics and Machine Learning Superstars

As regular readers of this blog know, I’ve migrated to a weekly (or potentially longer) topic focus, and this week’s topic is variable selection.

And the next planned post in the series will compare and contrast ridge regression and the LASSO (least absolute shrinkage and selection operator). There also are some new results for the LASSO. But all this takes time and is always better when actual computations can be accomplished to demonstrate points.

But in researching this, I’ve come to a deeper appreciation of the Tibshiranis.

Robert Tibshirani was an early exponent of the LASSO and has probably, as much as anyone, helped integrate the LASSO into standard statistical procedures.

Here’s his picture from Wikipedia.

You might ask why put his picuture up, and my answer is that Professor Robert Tibshirani (Stanford) has a son Ryan Tibshirani, whose picture is just below.

Ryan Tibsharani has a great Data Mining course online from Carnegie Mellon, where he is an Assistant Professor.

Professor Ryan Tibshirani’s Spring 2013 a Data Mining course can be found at http://www.stat.cmu.edu/~ryantibs/datamining/

Reviewing Ryan Tibsharani’s slides is very helpful in getting insight into topics like cross validation, ridge regression and the LASSO.

And let us not forget Professor Ryan Tibshirani is author of essential reading about how to pick your target in darts, based on your skill level (hint – don’t go for the triple-20 unless you are good).

Free Books on Machine Learning and Statistics

Robert Tibshirani et al’s text – Elements of Statistical Learning is now in the 10th version and is available online free here.

But the simpler An Introduction to Statistical Leaning is also available for an online download of a PDF file here. This is the corrected 4th printing. The book, which I have been reading today, is really dynamite – an outstanding example of scientific exposition and explanation.

These guys and their collaborators are truly gifted teachers. They create windows into new mathematical and statistical worlds, as it were.