Layman’s abstract for Canadian Journal of Statistics article on Imputation and Likelihood Methods for Matrix-Variate Logistic Regression with Response Misclassification

Each week, we publish layman’s abstracts of new articles from our prestigious portfolio of journals in statistics. The aim is to highlight the latest research to a broader audience in an accessible format.
The article featured today is from the Canadian Journal of Statistics with the full article now available to read here.
Fang, J. and Yi, G.Y. (2021), Imputation and likelihood methods for matrix-variate logistic regression with response misclassification. Can J Statistics, 49: 1298-1316.

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, such a model is impaired by the presence of the response misclassification. It is imperative to account for the misclassification effects when employing matrix-variate logistic regression to handle such data. In this paper, the authors develop two inferential methods which account for the misclassification effects. The first method, called an imputation method, roots in the score function derived from the misclassification-free context, and replaces the involved response variable with an unbiased pseudo-response variable that is expressed in terms of the observed surrogate measurement. The second method is to directly derive the likelihood function for the observed response surrogate and then conduct estimation accordingly. the authors’ development is carried out for two settings where misclassification rates are either known or estimated from validation data. The proposed methods are justified both theoretically and empirically. We analyze the Breast Cancer Wisconsin (Prognostic) data with the proposed methods.

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