Nonparametric Identification of Copula Structures

• 成果类型: Article
• 署名作者: Li, Bo; Genton, Marc G.
• 署名单位: Purdue University System; Purdue University; King Abdullah University of Science & Technology
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1080/01621459.2013.787083
• 发表日期: 2013
• 页码: 666-675
• 关键词: bivariate distributions; families; MODEL

摘要

We propose a unified framework for testing a variety of assumptions commonly made about the structure of copulas, including symmetry, radial symmetry, joint symmetry, associativity and Archimedeanity, and max-stability. Our test is nonparametric and based on the asymptotic distribution of the empirical copula process. We perform simulation experiments to evaluate our test and conclude that our method is reliable and powerful for assessing common assumptions on the structure of copulas, particularly when the sample size is moderately large. We illustrate our testing approach on two datasets.