Regularized matrix-variate logistic regression with response subject to misclassification

Abstract

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.