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

    Research output: Contribution to journalArticlepeer-review

    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

    Keywords

    • Genetic algorithms
    • Minimum information method
    • Pair-copula

    ASJC Scopus subject areas

    • Statistics and Probability
    • Modelling and Simulation

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