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A Maximum Entropy Copula Model for Mixed Data: Representation, Estimation, and Applications

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  • Subhadeep

    (DEEP)

  • Mukhopadhyay

Abstract

A new nonparametric model of maximum-entropy (MaxEnt) copula density function is proposed, which offers the following advantages: (i) it is valid for mixed random vector. By `mixed' we mean the method works for any combination of discrete or continuous variables in a fully automated manner; (ii) it yields a bonafide density estimate with intepretable parameters. By `bonafide' we mean the estimate guarantees to be a non-negative function, integrates to 1; and (iii) it plays a unifying role in our understanding of a large class of statistical methods. Our approach utilizes modern machinery of nonparametric statistics to represent and approximate log-copula density function via LP-Fourier transform. Several real-data examples are also provided to explore the key theoretical and practical implications of the theory.

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  • Subhadeep & Mukhopadhyay, 2021. "A Maximum Entropy Copula Model for Mixed Data: Representation, Estimation, and Applications," Papers 2108.09438, arXiv.org, revised Aug 2022.
    • Handle: RePEc:arx:papers:2108.09438
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    References listed on IDEAS

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    1. Subhadeep Mukhopadhyay & Emanuel Parzen, 2020. "Nonparametric universal copula modeling," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 36(1), pages 77-94, January.
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    Cited by:

    1. Subhadeep Mukhopadhyay, 2023. "Abductive Inference and C. S. Peirce: 150 Years Later," Journal of Quantitative Economics, Springer;The Indian Econometric Society (TIES), vol. 21(1), pages 123-149, March.

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