Comparison of non-nested models under a general measure of distance

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Comparison of non-nested models under a general measure of distance

TitleComparison of non-nested models under a general measure of distance
Publication TypeJournal Article
Year of Publication2016
AuthorsNg, CT, Joe, H
JournalJournal of Statistical Planning and Inference
Volume170
Pagination166-185
Date PublishedMAR
Type of ArticleArticle
ISSN0378-3758
KeywordsComposite likelihood, Copula, Large deviation theory, Model comparison, Model misspecification
AbstractAs a supplement to summary statistics of information criteria, the closeness of two or more competing non-nested models can be compared under a procedure that is more general than that proposed in Vuong (1989); measures of closeness other than the Kullback-Leibler divergence are allowed. Large deviation theory is used to obtain a bound of the power of rejecting the null hypothesis that the two models are equally close to the true model. Such a bound can be expressed in terms of a constant gamma is an element of [0, 1); gamma can be computed empirically without any knowledge of the data generating mechanism. Additionally, based on the constant gamma, the procedures constructed based on different measures of distance can be compared on their abilities to conclude a difference between two models. (C) 2015 Elsevier B.V. All rights reserved.
DOI10.1016/j.jspi.2015.10.004