Measurement error in covariates has been extensively studied in many conventional regression settings where covariate information is typically expressed in a vector form. However, there has been little work on error-prone matrix-variate data, which commonly arise from studies with imaging, spatial-temporal structures, etc. We consider analysis of error-contaminated matrix-variate data. We particularly focus on matrix-variate logistic measurement error models. We examine the biases induced from naive analysis which ignores measurement error in matrix-variate data. Two measurement error correction methods are developed to adjust for measurement error effects. The proposed methods are justified both theoretically and empirically. We analyse an electroencephalography dataset with the proposed methods.

中文翻译：

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具有测量误差的矩阵变量逻辑回归

在许多常规回归设置中，对协变量的测量误差进行了广泛研究，在这些环境中，协变量信息通常以矢量形式表示。但是，对容易出错的矩阵变量数据的研究很少，这些数据通常来自对成像，时空结构等的研究。我们考虑对受错误污染的矩阵变量数据进行分析。我们特别关注矩阵变量逻辑测量误差模型。我们检查了天真的分析引起的偏差，该偏差忽略了矩阵变量数据中的测量误差。开发了两种测量误差校正方法来针对测量误差影响进行调整。所提出的方法在理论上和经验上都是合理的。我们用提出的方法分析脑电图数据集。