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Least Angle Regression is a promising technique for variable selection applications, offering a nice alternative to stepwise regression. It provides an explanation for the similar behavior of LASSO ($\ell_1$-penalized regression) and forward stagewise regression, and provides a fast implementation of both. The idea has caught on rapidly, and sparked a great deal of research interest. In this paper, we give an overview of Least Angle Regression and the current state of related research., Comment: Published in at http://dx.doi.org/10.1214/08-SS035 the Statistics Surveys (http://www.i-journals.org/ss/) by the Institute of Mathematical Statistics (http://www.imstat.org)

A multivariate analysis of gamma-ray burst (GRB) bulk properties is presented to discriminate between distinct classes of GRBs. Several variables representing burst duration, fluence and spectral hardness are considered. Two multivariate clustering procedures are used on a sample of 797 bursts from the Third BATSE Catalog: a nonparametric average linkage hierarchical agglomerative clustering procedure validated with Wilks' $\Lambda^*$ and other MANOVA tests; and a parametric maximum likelihood model-based clustering procedure assuming multinormal populations calculated with the EM Algorithm and validated with the Bayesian Information Criterion. The two methods yield very similar results. The BATSE GRB population consists of three classes with the following Duration/Fluence/Spectrum bulk properties: Class I with long/bright/intermediate bursts, Class II with short/hard/faint bursts, and Class III with intermediate/intermediate/soft bursts. One outlier with poor data is also present. Classes I and II correspond to those reported by Kouveliotou et al. (1993), but Class III is clearly defined here for the first time., Comment: 24 pages, 5 figure, 7 tables. Submitted to the Astrophysical Journal in February 1998