Archive for Bayesian lasso

Monte Carlo Statistical Methods third edition

Posted in Books, R, Statistics, University life with tags , , , , , , , , , , , , , on September 23, 2010 by xi'an

Last week, George Casella and I worked around the clock on starting the third edition of Monte Carlo Statistical Methods by detailing the changes to make and designing the new table of contents. The new edition will not see a revolution in the presentation of the material but rather a more mature perspective on what matters most in statistical simulation:

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The day I invented Bayesian Lasso…

Posted in Books, Statistics with tags , , , on August 16, 2010 by xi'an

George Casella remarked to me last month in Padova that, once he and Trevor Park published The Bayesian Lasso in JASA, they received many claims for prior discovery of “Bayesian Lasso”! So, as a joke, let me add my claim as well! Indeed, in the first (1994) edition of The Bayesian Choice, I included an example in Chapter 4 (Example 4.2) about the fact that using a double exponential prior along a Cauchy likelihood was producing a zero MAP (maximum a posteriori) estimate. Isn’t that the essence of the Bayesian lasso?! Of course, as you can still check in the current edition, the example was intended as a counter-example to the use of MAP estimates, not as an argument about the parsimony induced by double exponential priors. (Exercice 4.6 in both editions builds upon this example to notice that, with a small enough scale parameter, the absolute shrinkage to zero vanishes.) I thus lost the opportunity of “inventing” the Bayesian Lasso! To my shame, I must add that in the even earlier 1992 French edition of the book, I made a mistake in the derivation of the MAP and hence completely missed the point!!!