A Maximum Entropy Copula Model for Mixed Data: Representation, Estimation, and Applications
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.
Cite
@article{arxiv.2108.09438,
title = {A Maximum Entropy Copula Model for Mixed Data: Representation, Estimation, and Applications},
author = {Subhadeep and Mukhopadhyay},
journal= {arXiv preprint arXiv:2108.09438},
year = {2022}
}
Comments
Revised and accepted version. Dedication: This paper is dedicated to E. T. Jaynes, the originator of the Maximum Entropy Principle, for his birth centenary. And to the memory of Leo Goodman, a transformative legend of Categorical Data Analysis. This paper is inspired in part to demonstrate how these two modeling philosophies can be connected and united in some ways