A maximum entropy copula model for mixed data: representation, estimation and applications. Issue 4 (2nd October 2022)

• Record Type: Journal Article
• Authors: Mukhopadhyay, Subhadeep
• Is Part Of: Journal of nonparametric statistics. Volume 34:Issue 4(2022)
• Page Start: 1036
• Page End: 1062
• Publication Date: 2022-10-02
• Subjects: Maximum entropy -- self-adaptive copula model -- LP-Fourier transform -- categorical data analysis -- copula-logistic regression -- united statistical learning; 62G05 -- 62H05 -- 94A17; Nonparametric statistics -- Periodicals; 519.5
• DOI: 10.1080/10485252.2022.2117914
• Languages: English
• ISSNs: 1048-5252

Abstract

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 ) . 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.