期刊
ENTROPY
卷 20, 期 6, 页码 -出版社
MDPI
DOI: 10.3390/e20060464
关键词
model assessment; statistical distance; non-quadratic distance; total variation; mixture index of fit; Kullback-Leibler distance; divergence measure
One 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.
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