Matrix-variate logistic regression is useful in facilitating the relationship between the binary response and complex-featured matrix-variates arising commonly from medical imaging research. However, standard inference procedures based on such a model are impaired by the presence of the response misclassification as well as inactive covariates. It is imperative to account for the misclassification effects and select active covariates when employing matrix-variate logistic regression to analyze such data. In this paper, we develop penalized unbiased estimating functions using the smoothly clipped absolute deviation (SCAD) penalty to address the sparsity of matrix-variate data as well as the response misclassification effects. The proposed methods are justified both theoretically and numerically. We analyze the Breast Cancer Wisconsin data with the proposed methods.

矩阵变量逻辑回归有助于促进医学成像研究中常见的二元响应与复杂特征矩阵变量之间的关系。然而，基于这种模型的标准推理程序因响应错误分类以及非活动协变量的存在而受到损害。在使用矩阵变量逻辑回归分析此类数据时，必须考虑误分类效应并选择活动协变量。在本文中，我们使用平滑剪裁绝对偏差 (SCAD) 惩罚来开发惩罚无偏估计函数，以解决矩阵变量数据的稀疏性以及响应错误分类的影响。所提出的方法在理论上和数值上都是合理的。