Model Confidence Set Based on Kullback-Leibler Divergence Distance

Consider the problem of estimating true density, h(.) based upon a random sample X1,…, Xn. In general, h(.)is approximated using an appropriate in some sense, see below) model f?(x). This article using Vuongʹs (1989) test along with a collection of k( > 2) non-nested models constructs a set of appropriate models, say model confidence set, for unknown model h(.).Application of such confidence set has been confirmed through a simulation study.