A maximum entropy copula model for mixed data: representation, estimation and applications

Subhadeep Mukhopadhyay

Journal of Nonparametric Statistics, 2022, vol. 34, issue 4, 1036-1062

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 for mixed $ (X,Y) $ (X,Y). Our approach utilises 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.

Date: 2022
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DOI: 10.1080/10485252.2022.2117914

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