混合数据:代表性、估计和应用 (A Maximum Entropy Copula Model for Mixed Data: Representation, Estimation, and Applications)

from arxiv, Two Legends from Two Different Cultures: 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

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.

翻译：提出了一个新的非参数模型,即最大有机性(MAxEnt)合金密度功能,它具有以下优势:(一) 它适用于混合随机矢量。“混合”是指以完全自动方式组合离散或连续变量的方法;(二) 它产生具有可预变参数的正反值密度估计值。“bonafide”是指保证估计数是非负函数,与1结合;以及(三) 它在我们理解大类统计方法方面发挥统一作用。