Multivariate sparse group lasso for the multivariate multiple linear regression with an arbitrary group structure.
Multivariate sparse group lasso for the multivariate multiple linear regression with an arbitrary group structure.
复制标题
具有任意组结构的多元多线性回归的多元稀疏组拉索。
作者：
Li Y;Nan B;Zhu J
针对高维预测变量和高维响应变量的数据，提出了一种多元稀疏群套索变量选择和估计方法。该方法是通过一个惩罚的多元多元线性回归模型与一个任意组结构的回归系数矩阵。它适合许多生物学研究，可以检测多个性状和多个预测因子之间的关联，每个性状和每个预测因子都嵌入一些生物功能组，如基因，通路或大脑区域。该方法能够有效地去除不重要的群体以及重要群体中不重要的个体系数，特别是对于大p小n问题，并且在处理各种复杂的群体结构如重叠或嵌套或多级分层结构时是灵活的。该方法通过广泛的模拟与传统的套索和组套索方法的比较进行评估，并适用于eQTL关联研究。
We propose a multivariate sparse group lasso variable selection and estimation method for data with high-dimensional predictors as well as high-dimensional response variables. The method is carried out through a penalized multivariate multiple linear regression model with an arbitrary group structure for the regression coefficient matrix. It suits many biology studies well in detecting associations between multiple traits and multiple predictors, with each trait and each predictor embedded in some biological functioning groups such as genes, pathways or brain regions. The method is able to effectively remove unimportant groups as well as unimportant individual coefficients within important groups, particularly for large p small n problems, and is flexible in handling various complex group structures such as overlapping or nested or multilevel hierarchical structures. The method is evaluated through extensive simulations with comparisons to the conventional lasso and group lasso methods, and is applied to an eQTL association study.
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