Optimal Sparse Linear Prediction for Block-missing Multi-modality Data without Imputation.
Optimal Sparse Linear Prediction for Block-missing Multi-modality Data without Imputation.
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DOI：
10.1080/01621459.2019.1632079
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发表时间：
2020
影响因子：
3.7
通讯作者：
Liu Y
中科院分区：
文献类型：
--
作者：
Yu G;Li Q;Shen D;Liu Y
在现代科学研究中，数据通常是通过多种方式收集的。由于不同的模态可以提供互补的信息，因此使用多模态数据的统计预测方法比使用单一模态数据的统计预测方法具有更好的预测性能。然而，使用多模态数据的一个特殊挑战与块缺失数据有关。在实践中，由于中途退出或测量成本高，某些受试者可能完全失去对某一模态的观察。本文提出了一种基于多模态数据协方差（DISCOM）的直接稀疏回归方法。我们提出的DISCOM方法包括两个步骤，使用块缺失多模态预测器找到连续响应变量的最优线性预测。在第一步中，我们不是删除或输入缺失的数据，而是利用所有可用的信息来估计预测因子的协方差矩阵以及预测因子与响应变量之间的交叉协方差向量。提出的协方差矩阵的新估计是单位矩阵、模态内协方差矩阵和跨模态协方差矩阵的估计的线性组合。考虑了亚高斯和重尾情况下的灵活估计。第二步，基于估计的协方差矩阵和估计的交叉协方差向量，使用扩展lasso型估计器对最优线性预测中的系数进行稀疏估计。DISCOM有效使用的样本数量是具有两种模态的可用观测值的最小样本数量，这可能比具有所有模态的完整观测值的样本数量大得多。理论研究、模拟实例和阿尔茨海默病神经影像学倡议的实际应用证明了该方法的有效性。与现有方法的比较也表明了本文方法的优越性。
In modern scientific research, data are often collected from multiple modalities. Since different modalities could provide complementary information, statistical prediction methods using multi-modality data could deliver better prediction performance than using single modality data. However, one special challenge for using multi-modality data is related to block-missing data. In practice, due to dropouts or the high cost of measures, the observations of a certain modality can be missing completely for some subjects. In this paper, we propose a new DIrect Sparse regression procedure using COvariance from Multi-modality data (DISCOM). Our proposed DISCOM method includes two steps to find the optimal linear prediction of a continuous response variable using block-missing multi-modality predictors. In the first step, rather than deleting or imputing missing data, we make use of all available information to estimate the covariance matrix of the predictors and the cross-covariance vector between the predictors and the response variable. The proposed new estimate of the covariance matrix is a linear combination of the identity matrix, the estimates of the intra-modality covariance matrix and the cross-modality covariance matrix. Flexible estimates for both the sub-Gaussian and heavy-tailed cases are considered. In the second step, based on the estimated covariance matrix and the estimated cross-covariance vector, an extended Lasso-type estimator is used to deliver a sparse estimate of the coefficients in the optimal linear prediction. The number of samples that are effectively used by DISCOM is the minimum number of samples with available observations from two modalities, which can be much larger than the number of samples with complete observations from all modalities. The effectiveness of the proposed method is demonstrated by theoretical studies, simulated examples, and a real application from the Alzheimer’s Disease Neuroimaging Initiative. The comparison between DISCOM and some existing methods also indicates the advantages of our proposed method.
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影响因子：
1.5
作者：
Lounici, Karim
通讯作者：
Lounici, Karim
DOI：
10.1111/rssb.12166
发表时间：
2017-01
期刊：
Journal of the Royal Statistical Society. Series B, Statistical methodology
影响因子：
--
作者：
Fan J;Li Q;Wang Y
通讯作者：
Wang Y
影响因子：
1.6
作者：
Ledoit, O;Wolf, M
通讯作者：
Wolf, M
影响因子：
1.6
作者：
Cai, T. Tony;Zhang, Anru
通讯作者：
Zhang, Anru
影响因子：
4.5
作者：
Meinshausen, Nicolai;Yu, Bin
通讯作者：
Yu, Bin