Hypothesis tests in ordinal predictive models with optimal accuracy
Yuyang Liu,
Shan Luo,
Jialiang Li
Abstract:In real-world applications involving multi-class ordinal discrimination, a common approach is to aggregate multiple predictive variables into a linear combination, aiming to develop a classifier with high prediction accuracy. Assessment of such multi-class classifiers often utilizes the hypervolume under ROC manifolds (HUM). When dealing with a substantial pool of potential predictors and achieving optimal HUM, it becomes imperative to conduct appropriate statistical inference. However, prevalent methodologies… Show more
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