Robust multi-outcome regression with correlated covariate blocks using fused LAD-lasso.

IF 1.2 4区 数学 Q2 STATISTICS & PROBABILITY
Journal of Applied Statistics Pub Date : 2024-10-11 eCollection Date: 2025-01-01 DOI:10.1080/02664763.2024.2414346
Jyrki Möttönen, Tero Lähderanta, Janne Salonen, Mikko J Sillanpää
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引用次数: 0

Abstract

Lasso is a popular and efficient approach to simultaneous estimation and variable selection in high-dimensional regression models. In this paper, a robust fused 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 illustrated by extensive simulations using BIC-type criteria for model selection. The method is also applied to a real-life skewed data on retirement behavior with longitudinal heteroscedastic explanatory variables.

采用融合ladl -套索的相关协变量块鲁棒多结果回归。
Lasso是一种在高维回归模型中进行同时估计和变量选择的有效方法。本文提出了一种鲁棒的多结果融合LAD-lasso方法,解决了非正态结果分布和离群观测值的挑战。从空间或时间、光谱波段或基因组位置测量的协变量数据往往具有由于测量协变量之间的距离而产生的自然相关结构。所提出的多结果方法包括通过组融合惩罚来处理这些协变量块,该方法通过惩罚相邻回归系数向量的差异来鼓励它们之间的相似性,例如,在顺序数据情况下。采用bic类型的模型选择标准进行了广泛的仿真,说明了所提出方法的特性。该方法还应用于具有纵向异方差解释变量的现实生活中有关退休行为的偏斜数据。
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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来源期刊
Journal of Applied Statistics
Journal of Applied Statistics 数学-统计学与概率论
CiteScore
3.40
自引率
0.00%
发文量
126
审稿时长
6 months
期刊介绍: Journal of Applied Statistics provides a forum for communication between both applied statisticians and users of applied statistical techniques across a wide range of disciplines. These areas include business, computing, economics, ecology, education, management, medicine, operational research and sociology, but papers from other areas are also considered. The editorial policy is to publish rigorous but clear and accessible papers on applied techniques. Purely theoretical papers are avoided but those on theoretical developments which clearly demonstrate significant applied potential are welcomed. Each paper is submitted to at least two independent referees.
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