Wu Yichao
Department of Statistics, North Carolina State University, Raleigh, NC 27695, USA.
Stat Sin. 2012;22:27-294. doi: 10.5705/ss.2010.107.
For least squares regression, Efron et al. (2004) proposed an efficient solution path algorithm, the least angle regression (LAR). They showed that a slight modification of the LAR leads to the whole LASSO solution path. Both the LAR and LASSO solution paths are piecewise linear. Recently Wu (2011) extended the LAR to generalized linear models and the quasi-likelihood method. In this work we extend the LAR further to handle Cox's proportional hazards model. The goal is to develop a solution path algorithm for the elastic net penalty (Zou and Hastie (2005)) in Cox's proportional hazards model. This goal is achieved in two steps. First we extend the LAR to optimizing the log partial likelihood plus a fixed small ridge term. Then we define a path modification, which leads to the solution path of the elastic net regularized log partial likelihood. Our solution path is exact and piecewise determined by ordinary differential equation systems.
对于最小二乘回归,埃弗龙等人(2004年)提出了一种高效的求解路径算法,即最小角回归(LAR)。他们表明,对LAR进行轻微修改会得到整个LASSO求解路径。LAR和LASSO求解路径都是分段线性的。最近,吴(2011年)将LAR扩展到广义线性模型和拟似然方法。在这项工作中,我们进一步将LAR扩展以处理考克斯比例风险模型。目标是为考克斯比例风险模型中的弹性网惩罚(邹和哈斯蒂(2005年))开发一种求解路径算法。这个目标分两步实现。首先,我们将LAR扩展到优化对数偏似然加上一个固定的小岭项。然后我们定义一种路径修正,这会得到弹性网正则化对数偏似然的求解路径。我们的求解路径是精确的,并且由常微分方程组分段确定。