Bayesian analysis for matrix-variate logistic regression with/without response misclassification

Matrix-variate logistic regression is useful in facilitating the relationship between the binary response and matrix-variates which arise commonly from medical imaging research. However, inference based on such a model is impaired by the presence of the response misclassification and spurious covariates It is imperative to account for the misclassification effects and select active covatiates when employing matrix-variate logistic regression to handle such data. In this paper, we develop Bayesian inferential methods with the horse-shoe prior. We numerically examine the biases induced from the naive analysis which ignores misclassification of responses. The performance of the proposed methods is justified empirically and their usage is illustrated by the application to the Lee Silverman Voice Treatment (LSVT) Companion data.