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The copula information criteria

Grønneberg, Steffen; Hjort, Nils Lid
Journal article, Peer reviewed
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The copula information criteria 2014.pdf (746.6Kb)
URI
http://hdl.handle.net/11250/226105
Date
2014
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  • Scientific articles [1357]
Original version
Scandinavian Journal of Statistics, 41(2014)2: 436-459   10.1111/sjos.12042
Abstract
We derive two types of Akaike information criterion (AIC)-like model-selection formulae for the semiparametric pseudo-maximum likelihood procedure. We first adapt the arguments leading to the original AIC formula, related to empirical estimation of a certain Kullback–Leibler information distance. This gives a significantly different formula compared with the AIC, which we name the copula information criterion. However, we show that such a model-selection procedure cannot exist for copula models with densities that grow very fast near the edge of the unit cube. This problem affects most popular copula models. We then derive what we call the cross-validation copula information criterion, which exists under weak conditions and is a first-order approximation to exact cross validation. This formula is very similar to the standard AIC formula but has slightly different motivation. A brief illustration with real data is given.
Description
This is the author’s accepted, refereed and final manuscript to the article.
Publisher
Wiley
Journal
Scandinavian Journal of Statistics

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