Optimizing minimum information pair-copula using genetic algorithm to select optimal basis functions

Omid Chatrabgoun, Alireza Daneshkhah, M Esmaeilbeigi

Research output: Contribution to journalArticle

Abstract

Constructing pair-copula using the minimum information approach is an appropriate and flexible way to survey the dependency structure between variables of interest. Minimum information pair-copula method approximates multivariate copula by applying some constraints between desired variables that are elicited from the data itself or experts’ judgment. In minimum information pair-copula, selecting basis constraints is a challenge. In this article, we apply genetic algorithms as a heuristic way to select basis constraints to optimize approximated pair-copula. The results gained show that our method optimizes model selection criteria and lead to better pair-copula approximation. Finally, we apply our proposed method to approximate pair-copula density in real dataset.
Original languageEnglish
Pages (from-to)494-505
Number of pages12
JournalCommunications in Statistics - Simulation and Computation
Volume48
Issue number2
Early online date26 Oct 2017
DOIs
Publication statusPublished - 7 Feb 2019

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Copula
Basis Functions
Genetic algorithms
Genetic Algorithm
Optimise
Expert Judgment
Model Selection Criteria
Heuristics
Approximation

Keywords

  • Genetic algorithms
  • Minimum information method
  • Pair-copula

ASJC Scopus subject areas

  • Statistics and Probability
  • Modelling and Simulation

Cite this

Optimizing minimum information pair-copula using genetic algorithm to select optimal basis functions. / Chatrabgoun, Omid; Daneshkhah, Alireza; Esmaeilbeigi, M.

In: Communications in Statistics - Simulation and Computation, Vol. 48, No. 2, 07.02.2019, p. 494-505.

Research output: Contribution to journalArticle

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