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A Universal Approximate Cross-Validation Criterion for Regular Risk Functions
The International Journal of Biostatistics
AbstractSelection of estimators is an essential task in modeling. A general framework is that the estimators of a distribution are obtained by minimizing a function (the estimating function) and assessed using another function (the assessment function). A classical case is that both functions estimate an information risk (specifically cross-entropy); this corresponds to using maximum likelihood estimators and assessing them by Akaike information criterion (AIC). In more general cases, thedoi:10.1515/ijb-2015-0004 pmid:25849800 fatcat:wncoag3qzvbv7pvubz5ahtotiq