An evaluation of KL-optimum designs to discriminate between rival copula models

Risultato della ricerca: Contributo in libroContributo a convegno

Abstract

The problem of model discrimination has prompted a great amount of research over last years. According to the specific characteristics of the rival models (nested, non-nested, linear or not) different optimum criteria have been proposed to select design points with the aim to discriminate between rival models. Ds-, T- and KL-criteria are the most known. Up to our knowledge, in the literature there is not any study to evaluate the performance of these discrimination criteria. In this work, via a simulation study and focusing on rival copula models, we analyze the performance of the KL-optimum design applying the likelihood ratio test for nonnested models.
Lingua originaleEnglish
Titolo della pubblicazione ospiteBook of Short Papers SIS 2018
Pagine1425-1430
Numero di pagine6
Stato di pubblicazionePubblicato - 2018
Evento49H SCIENTIFIC MEETING OF THE ITALIAN STATISTICAL SOCIETY-SIS 2018 - Palermo
Durata: 20 giu 201822 giu 2018

Convegno

Convegno49H SCIENTIFIC MEETING OF THE ITALIAN STATISTICAL SOCIETY-SIS 2018
CittàPalermo
Periodo20/6/1822/6/18

Keywords

  • Copula model
  • Cox’s test
  • Optimal experimental design

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