JSM 2004 - Toronto

Abstract #300207

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Activity Number: 364
Type: Invited
Date/Time: Wednesday, August 11, 2004 : 2:00 PM to 3:50 PM
Sponsor: Section on Statistical Computing
Abstract - #300207
Title: On Algorithms for Solving Least Squares Problems under an L1-type Penalty or Constraint
Author(s): Berwin A. Turlach*+
Companies: University of Western Australia
Address: School of Mathematics and Statistics (M019), Crawley, WA 6009, Australia
Keywords: constrained least squares problem ; constrained regression ; convex programming ; interior-point algorithm ; quadratic programming ; subset selection
Abstract:

Tibshirani (1996) proposed the least absolute shrinkage and selection operator (LASSO) which estimates a vector of regression coefficients by minimizing the residual sum of squares subject to a constraint (penalty) on the sum of the absolute values of the coefficient estimates. We shall discuss several algorithms that can be used to calculate the LASSO solution. We will discuss the individual strengths and weaknesses of these algorithms by commenting, among others, on the accuracy of the calculated solution, the ease of their implementation (a somewhat subjective criterion}, and the ease with which they can be extended to similar but more complex problems. This talk is based on joint work with M.R. Osborne, B. Presnell, W.N. Venables, and S.J. Wright.

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