Maintaining tail dependence in data shuffling using t copula

Mario Trottini, Krish Muralidhar and Rathindra Sarathy

Statistics & Probability Letters, 2011, vol. 81, issue 3, 420-428

Abstract: Data shuffling is a recently proposed technique for masking numerical data where the confidential values are shuffled between records while maintaining all monotonic relationships between the variables in the data set. Data shuffling is based on the multivariate normal copula which assumes that there is no tail dependence in the data set. In many practical situations, however, tail dependence plays a crucial role in decision making. Hence, it is desirable that the data masking procedure be capable of preserving tail dependence when present. In this study, we provide a new data shuffling approach based on t copulas that is capable of maintaining tail dependence in the masked data in a large number of applications.

Keywords: Statistical; confidentiality; Copulas; Data; shuffling; Disclosure; risk; Data; dissemination (search for similar items in EconPapers)
Date: 2011
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0167-7152(10)00342-1
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:stapro:v:81:y:2011:i:3:p:420-428

Ordering information: This journal article can be ordered from
http://www.elsevier.com/wps/find/supportfaq.cws_home/regional
https://shop.elsevie ... _01_ooc_1&version=01

Access Statistics for this article

Statistics & Probability Letters is currently edited by Somnath Datta and Hira L. Koul

More articles in Statistics & Probability Letters from Elsevier
Bibliographic data for series maintained by Catherine Liu ().