Non-Quadratic Distances in Model Assessment
AbstractOne natural way to measure model adequacy is by using statistical distances as loss functions. A related fundamental question is how to construct loss functions that are scientifically and statistically meaningful. In this paper, we investigate non-quadratic distances and their role in assessing the adequacy of a model and/or ability to perform model selection. We first present the definition of a statistical distance and its associated properties. Three popular distances, total variation, the mixture index of fit and the Kullback-Leibler distance, are studied in detail, with the aim of understanding their properties and potential interpretations that can offer insight into their performance as measures of model misspecification. A small simulation study exemplifies the performance of these measures and their application to different scientific fields is briefly discussed. View Full-Text
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Markatou, M.; Chen, Y. Non-Quadratic Distances in Model Assessment. Entropy 2018, 20, 464.
Markatou M, Chen Y. Non-Quadratic Distances in Model Assessment. Entropy. 2018; 20(6):464.Chicago/Turabian Style
Markatou, Marianthi; Chen, Yang. 2018. "Non-Quadratic Distances in Model Assessment." Entropy 20, no. 6: 464.
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