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一种基于常微分方程的求解路径算法。

An ordinary differential equation based solution path algorithm.

作者信息

Wu Yichao

机构信息

Department of Statistics, North Carolina State University, 2311 Stinson Drive, Raleigh, NC 27695.

出版信息

J Nonparametr Stat. 2011;23(1):185-199. doi: 10.1080/10485252.2010.490584.

Abstract

Efron, Hastie, Johnstone and Tibshirani (2004) proposed Least Angle Regression (LAR), a solution path algorithm for the least squares regression. They pointed out that a slight modification of the LAR gives the LASSO (Tibshirani, 1996) solution path. However it is largely unknown how to extend this solution path algorithm to models beyond the least squares regression. In this work, we propose an extension of the LAR for generalized linear models and the quasi-likelihood model by showing that the corresponding solution path is piecewise given by solutions of ordinary differential equation systems. Our contribution is twofold. First, we provide a theoretical understanding on how the corresponding solution path propagates. Second, we propose an ordinary differential equation based algorithm to obtain the whole solution path.

摘要

埃弗龙、哈斯蒂、约翰斯通和蒂布希拉尼(2004年)提出了最小角回归(LAR),一种用于最小二乘回归的解路径算法。他们指出,对LAR进行轻微修改即可得到套索回归(LASSO)(蒂布希拉尼,1996年)的解路径。然而,如何将这种解路径算法扩展到最小二乘回归之外的模型,在很大程度上仍然未知。在这项工作中,我们通过证明相应的解路径由常微分方程组的解分段给出,提出了一种针对广义线性模型和拟似然模型的LAR扩展。我们的贡献有两方面。第一,我们提供了关于相应解路径如何传播的理论理解。第二,我们提出了一种基于常微分方程的算法来获得整个解路径。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a92f/3083079/6de992230e06/nihms-250294-f0001.jpg

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