Sparse common and distinctive covariates regression
Sparse common and distinctive covariates regression
复制标题
稀疏共同变量和独特协变量回归
DOI：
10.1002/cem.3270
复制
发表时间：
2020
影响因子：
2.4
通讯作者：
K. Van Deun
中科院分区：
文献类型：
--
作者：
Soogeun Park;E. Ceulemans;K. Van Deun
在化学计量学中，具有来自多个来源的关于相同观察单元和相同准则的大的预测因子集合变得越来越普遍。当分析这些数据时，化学计量学家通常有多个目标：预测标准，变量选择和识别与单个预测源或几个源联合相关的潜在过程。现有的方法提供了关于前两个目标的解决方案，即针对单个预测变量块来揭示预测机制和其中的相关变量，但是在多尺度设置中揭示联合和独特的预测机制和其中的相关变量的挑战仍然需要解决。为此，我们提出了一个多块扩展的主要协变量回归，旨在找到复杂的机制，其中可能涉及到几个或单个来源;一起考虑，这些机制预测的结果感兴趣。我们称这种方法为稀疏共同和独特协变量回归（SCD‐CovR）。通过模拟研究，我们证明了SCD‐CovR与相关方法相比，提供了有竞争力的解决方案。该方法还示出了通过一个公开可用的数据集的应用程序。
Having large sets of predictors from multiple sources concerning the same observation units and the same criterion is becoming increasingly common in chemometrics. When analyzing such data, chemometricians often have multiple objectives: prediction of the criterion, variable selection, and identification of underlying processes associated to individual predictor sources or to several sources jointly. Existing methods offer solutions regarding the first two aims of uncovering the predictive mechanisms and relevant variables therein for a single block of predictor variables, but the challenge of uncovering joint and distinctive predictive mechanisms and the relevant variables therein in the multisource setting still needs to be addressed. To this end, we present a multiblock extension of principal covariates regression that aims to find the complex mechanisms in which several or single sources may be involved; taken together, these mechanisms predict an outcome of interest. We call this method sparse common and distinctive covariates regression (SCD‐CovR). Through a simulation study, we demonstrate that SCD‐CovR provides competitive solutions when compared with related methods. The method is also illustrated via an application to a publicly available dataset.
DOI：
10.1198/016214506000000735
发表时间：
2006-12-01
影响因子：
3.7
作者：
Zou, Hui
通讯作者：
Zou, Hui