Multi-response Regression for Block-missing Multi-modal Data without Imputation.

IF 16.4 1区 化学 Q1 CHEMISTRY, MULTIDISCIPLINARY
Haodong Wang, Quefeng Li, Yufeng Liu
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引用次数: 0

Abstract

Multi-modal data are prevalent in many scientific fields. In this study, we consider the parameter estimation and variable selection for a multi-response regression using block-missing multi-modal data. Our method allows the dimensions of both the responses and the predictors to be large, and the responses to be incomplete and correlated, a common practical problem in high-dimensional settings. Our proposed method uses two steps to make a prediction from a multi-response linear regression model with block-missing multi-modal predictors. In the first step, without imputing missing data, we use all available data to estimate the covariance matrix of the predictors and the cross-covariance matrix between the predictors and the responses. In the second step, we use these matrices and a penalized method to simultaneously estimate the precision matrix of the response vector, given the predictors, and the sparse regression parameter matrix. Lastly, we demonstrate the effectiveness of the proposed method using theoretical studies, simulated examples, and an analysis of a multi-modal imaging data set from the Alzheimer's Disease Neuroimaging Initiative.

针对块缺失多模态数据的多响应回归,无需估算。
多模态数据在很多科学领域都很普遍。在本研究中,我们考虑了使用块缺失多模态数据进行多响应回归的参数估计和变量选择。我们的方法允许响应和预测因子的维度都很大,并且响应是不完整和相关的,这是高维环境中常见的实际问题。我们提出的方法采用两个步骤,对带有块缺失多模态预测因子的多响应线性回归模型进行预测。第一步,在不计算缺失数据的情况下,我们使用所有可用数据来估计预测因子的协方差矩阵以及预测因子与响应之间的交叉协方差矩阵。在第二步中,我们使用这些矩阵和一种惩罚性方法来同时估计响应向量的精度矩阵(给定预测因子)和稀疏回归参数矩阵。最后,我们通过理论研究、模拟示例以及对阿尔茨海默病神经成像计划多模态成像数据集的分析,证明了所提方法的有效性。
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
Accounts of Chemical Research
Accounts of Chemical Research 化学-化学综合
CiteScore
31.40
自引率
1.10%
发文量
312
审稿时长
2 months
期刊介绍: Accounts of Chemical Research presents short, concise and critical articles offering easy-to-read overviews of basic research and applications in all areas of chemistry and biochemistry. These short reviews focus on research from the author’s own laboratory and are designed to teach the reader about a research project. In addition, Accounts of Chemical Research publishes commentaries that give an informed opinion on a current research problem. Special Issues online are devoted to a single topic of unusual activity and significance. Accounts of Chemical Research replaces the traditional article abstract with an article "Conspectus." These entries synopsize the research affording the reader a closer look at the content and significance of an article. Through this provision of a more detailed description of the article contents, the Conspectus enhances the article's discoverability by search engines and the exposure for the research.
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