Title: A weighted model confidence set: applications to local and mixture model confidence sets

Authors: Amir T. Payandeh Najafabadi; Ghobad Barmalzan; Shahla Aghaei

Addresses: Department of Mathematical Sciences, Shahid Beheshti University, G.C. Evin, Tehran, Iran ' Department of Statistics, University of Zabol, Sistan and Baluchestan, Iran ' Department of Mathematical Sciences, Shahid Beheshti University, G.C. Evin, Tehran, Iran

Abstract: Using the Kullback-Leibler (KL) divergence along with Vuong's test, this article constructs a set of appropriate weighted models, say weighted model confidence set, for unknown true density h(·) which is a subset of a class of non-nested models. A weighted confidence set provides an appropriate model for random variable X which some part of its support conveys some important information about the underlying true model. Application of such a weighted model confidence set for local and mixture model confidence sets have been given. Two simulation studies have been conducted to show practical application of our findings.

Keywords: inference under constraints; Kullback-Leibler divergence; local goodness of fitness; mixture models; model confidence set.

DOI: 10.1504/IJMMNO.2017.086797

International Journal of Mathematical Modelling and Numerical Optimisation, 2017 Vol.8 No.2, pp.127 - 144

Accepted: 27 Mar 2017
Published online: 13 Sep 2017 *

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