Fast estimating data dependence structure via fuzzy Empirical Copula

Zhaojie Ju; Honghai Liu; Youlun Xiong

doi:10.1109/FUZZY.2009.5277186

Fast estimating data dependence structure via fuzzy Empirical Copula

Zhaojie Ju^*
, Honghai Liu
, Youlun Xiong

^*Corresponding author for this work

University of Portsmouth
Huazhong University of Science and Technology

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Scopus citations

Abstract

As a non-parametric algorithm, Empirical Copula is an effective way to estimate the dependence structure of high-dimension arbitrarily distributed data. However, it suffers from the problem of huge computation time because of its high computational complexity. In this paper, Fuzzy Empirical Copula is proposed to solve this problem by combining the Fuzzy Clustering by Local Approximation of Memberships (FLAME) with Empirical Copula. In the proposed algorithm, FLAME is extended from two-dimension data to high-dimension data and FLAME⁺ is implemented to identify the highest density objects which represent the original dataset, and then Empirical Copula is used to estimate its independence structure according to the new dataset. Case studies have been carried out to demonstrate the effectiveness of the Fuzzy Empirical Copula.

Original language	English
Title of host publication	2009 IEEE International Conference on Fuzzy Systems - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	882-887
Number of pages	6
ISBN (Print)	9781424435975
DOIs	https://doi.org/10.1109/FUZZY.2009.5277186
State	Published - 2009
Externally published	Yes
Event	18th IEEE International Conference on Fuzzy Systems, FUZZ-IEEE 2009 - Jeju Island, Korea, Republic of Duration: 20 Aug 2009 → 24 Aug 2009

Publication series

Name	IEEE International Conference on Fuzzy Systems
ISSN (Print)	1098-7584

Conference

Conference	18th IEEE International Conference on Fuzzy Systems, FUZZ-IEEE 2009
Country/Territory	Korea, Republic of
City	Jeju Island
Period	20/08/09 → 24/08/09

Keywords

Computation cost
Dependence structure
FLAME
Fuzzy Empirical Copula

Access to Document

10.1109/FUZZY.2009.5277186

Cite this

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title = "Fast estimating data dependence structure via fuzzy Empirical Copula",

abstract = "As a non-parametric algorithm, Empirical Copula is an effective way to estimate the dependence structure of high-dimension arbitrarily distributed data. However, it suffers from the problem of huge computation time because of its high computational complexity. In this paper, Fuzzy Empirical Copula is proposed to solve this problem by combining the Fuzzy Clustering by Local Approximation of Memberships (FLAME) with Empirical Copula. In the proposed algorithm, FLAME is extended from two-dimension data to high-dimension data and FLAME+ is implemented to identify the highest density objects which represent the original dataset, and then Empirical Copula is used to estimate its independence structure according to the new dataset. Case studies have been carried out to demonstrate the effectiveness of the Fuzzy Empirical Copula.",

keywords = "Computation cost, Dependence structure, FLAME, Fuzzy Empirical Copula",

author = "Zhaojie Ju and Honghai Liu and Youlun Xiong",

year = "2009",

doi = "10.1109/FUZZY.2009.5277186",

language = "英语",

isbn = "9781424435975",

series = "IEEE International Conference on Fuzzy Systems",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "882--887",

booktitle = "2009 IEEE International Conference on Fuzzy Systems - Proceedings",

address = "美国",

note = "18th IEEE International Conference on Fuzzy Systems, FUZZ-IEEE 2009 ; Conference date: 20-08-2009 Through 24-08-2009",

}

Ju, Z, Liu, H & Xiong, Y 2009, Fast estimating data dependence structure via fuzzy Empirical Copula. in 2009 IEEE International Conference on Fuzzy Systems - Proceedings., 5277186, IEEE International Conference on Fuzzy Systems, Institute of Electrical and Electronics Engineers Inc., pp. 882-887, 18th IEEE International Conference on Fuzzy Systems, FUZZ-IEEE 2009, Jeju Island, Korea, Republic of, 20/08/09. https://doi.org/10.1109/FUZZY.2009.5277186

Fast estimating data dependence structure via fuzzy Empirical Copula. / Ju, Zhaojie; Liu, Honghai; Xiong, Youlun.
2009 IEEE International Conference on Fuzzy Systems - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2009. p. 882-887 5277186 (IEEE International Conference on Fuzzy Systems).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Ju, Zhaojie

AU - Liu, Honghai

AU - Xiong, Youlun

PY - 2009

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N2 - As a non-parametric algorithm, Empirical Copula is an effective way to estimate the dependence structure of high-dimension arbitrarily distributed data. However, it suffers from the problem of huge computation time because of its high computational complexity. In this paper, Fuzzy Empirical Copula is proposed to solve this problem by combining the Fuzzy Clustering by Local Approximation of Memberships (FLAME) with Empirical Copula. In the proposed algorithm, FLAME is extended from two-dimension data to high-dimension data and FLAME+ is implemented to identify the highest density objects which represent the original dataset, and then Empirical Copula is used to estimate its independence structure according to the new dataset. Case studies have been carried out to demonstrate the effectiveness of the Fuzzy Empirical Copula.

AB - As a non-parametric algorithm, Empirical Copula is an effective way to estimate the dependence structure of high-dimension arbitrarily distributed data. However, it suffers from the problem of huge computation time because of its high computational complexity. In this paper, Fuzzy Empirical Copula is proposed to solve this problem by combining the Fuzzy Clustering by Local Approximation of Memberships (FLAME) with Empirical Copula. In the proposed algorithm, FLAME is extended from two-dimension data to high-dimension data and FLAME+ is implemented to identify the highest density objects which represent the original dataset, and then Empirical Copula is used to estimate its independence structure according to the new dataset. Case studies have been carried out to demonstrate the effectiveness of the Fuzzy Empirical Copula.

KW - Computation cost

KW - Dependence structure

KW - FLAME

KW - Fuzzy Empirical Copula

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DO - 10.1109/FUZZY.2009.5277186

M3 - 会议稿件

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SN - 9781424435975

T3 - IEEE International Conference on Fuzzy Systems

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BT - 2009 IEEE International Conference on Fuzzy Systems - Proceedings

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 18th IEEE International Conference on Fuzzy Systems, FUZZ-IEEE 2009

Y2 - 20 August 2009 through 24 August 2009

ER -

Fast estimating data dependence structure via fuzzy Empirical Copula

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Keywords

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