Least angle and ℓ_1 penalized regression: A review

02/07/2008
by   Tim Hesterberg, et al.
0

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 (ℓ_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.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
07/30/2020

Solar: a least-angle regression for accurate and stable variable selection in high-dimensional data

We propose a new least-angle regression algorithm for variable selection...
research
09/10/2012

Group descent algorithms for nonconvex penalized linear and logistic regression models with grouped predictors

Penalized regression is an attractive framework for variable selection p...
research
09/14/2019

Higher Order Refinements by Bootstrap in Lasso and other Penalized Regression Methods

Selection of important covariates and to drop the unimportant ones from ...
research
01/12/2014

Inference in High Dimensions with the Penalized Score Test

In recent years, there has been considerable theoretical development reg...
research
01/29/2018

Fast Penalized Regression and Cross Validation for Tall Data with the oem Package

A large body of research has focused on theory and computation for varia...
research
11/26/2013

A Blockwise Descent Algorithm for Group-penalized Multiresponse and Multinomial Regression

In this paper we purpose a blockwise descent algorithm for group-penaliz...
research
08/10/2017

When Does the First Spurious Variable Get Selected by Sequential Regression Procedures?

Applied statisticians use sequential regression procedures to produce a ...

Please sign up or login with your details

Forgot password? Click here to reset