论文标题
使用Fused lad-lasso使用相关的协变量块，可靠的多种结果回归
Robust multi-outcome regression with correlated covariate blocks using fused LAD-lasso
论文作者
论文摘要
Lasso是在高维回归模型中同时估计和可变选择的一种流行而有效的方法。在本文中，提出了一种强大的LAD-LASSO方法，以解决多种结果，以解决非正常结果分布和外出观察的挑战。从空间或时间或光谱带或基因组位置的测量协变量数据通常具有自然相关结构，该结构是由测量协变量之间的距离产生的。拟议的多结果方法包括通过组融合惩罚处理此类协变量块，这通过对邻近的回归系数矢量进行惩罚来鼓励相似性，例如在顺序数据情况下。首先通过广泛的模拟说明了所提出的方法的属性，其次，该方法应用于现实生活中的偏斜数据示例，示例具有异性解释变量。
Lasso is a popular and efficient approach to simultaneous estimation and variable selection in high-dimensional regression models. In this paper, a robust LAD-lasso method for multiple outcomes is presented that addresses the challenges of non-normal outcome distributions and outlying observations. Measured covariate data from space or time, or spectral bands or genomic positions often have natural correlation structure arising from measuring distance between the covariates. The proposed multi-outcome approach includes handling of such covariate blocks by a group fusion penalty, which encourages similarity between neighboring regression coefficient vectors by penalizing their differences for example in sequential data situation. Properties of the proposed approach are first illustrated by extensive simulations, and secondly the method is applied to a real-life skewed data example on retirement behavior with heteroscedastic explanatory variables.
