最小角和1惩罚回归:综述

IF 11 Q1 STATISTICS & PROBABILITY

Statistics Surveys Pub Date : 2008-02-07 DOI:10.1214/08-SS035

T. Hesterberg, Nam-Hee Choi, L. Meier, C. Fraley

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引用次数: 289

摘要

最小角回归对于变量选择应用来说是一种很有前途的技术，它为逐步回归提供了一个很好的替代方案。它为LASSO(11惩罚回归)和前向阶段回归的相似行为提供了解释，并提供了两者的快速实现。这个想法迅速流行起来，并引发了大量的研究兴趣。本文对最小角回归进行了概述，并对相关研究现状进行了综述。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Least angle and ℓ1 penalized regression: A review

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 (l1-penalized regression) and forward stagewise regression, and provides a fast imple- mentation 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.

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来源期刊

Statistics Surveys STATISTICS & PROBABILITY-

CiteScore

11.70

自引率

0.00%

发文量

期刊介绍： Statistics Surveys publishes survey articles in theoretical, computational, and applied statistics. The style of articles may range from reviews of recent research to graduate textbook exposition. Articles may be broad or narrow in scope. The essential requirements are a well specified topic and target audience, together with clear exposition. Statistics Surveys is sponsored by the American Statistical Association, the Bernoulli Society, the Institute of Mathematical Statistics, and by the Statistical Society of Canada.