Details
Original language | English |
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Title of host publication | 2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017 - Proceedings |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Pages | 1-9 |
Number of pages | 9 |
ISBN (electronic) | 9781538627266 |
ISBN (print) | 9781538627273 |
Publication status | Published - 2017 |
Event | 2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017 - Honolulu, United States Duration: 27 Nov 2017 → 1 Dec 2017 |
Abstract
In many practical situations, it is important to know the correlation between different quantities - finding correlations helps to gain insights into various relationships and phenomena, and helps to inform analysts. Often, there is not enough empirical data to experimentally determine all possible correlations. In such cases, a natural idea is to supplement this situation with expert estimates. Expert estimates are rather crude. So, to decide whether to act based on these estimates, it is desirable to know how accurate are expert estimates. In this paper, we propose several techniques for gauging this accuracy.
Keywords
- correlation, expert estimates, fuzzy sets, uncertainty, vagueness
ASJC Scopus subject areas
- Computer Science(all)
- Artificial Intelligence
- Computer Science(all)
- Computer Science Applications
- Mathematics(all)
- Control and Optimization
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2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2017. p. 1-9.
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - How Accurate Are Expert Estimations of Correlation?
AU - Beer, Michael
AU - Gong, Zitong
AU - Diaz De La O, Francisco Alejandro
AU - Kreinovich, Vladik
N1 - Funding information: This work was also supported in part by the National Science Foundation grants HRD-0734825 and HRD-1242122 (Cyber-ShARE Center of Excellence) and DUE-0926721, and by an award “UTEP and Prudential Actuarial Science Academy and Pipeline Initiative” from Prudential Foundation, and by a program of the China Scholarship Council. This work was performed when Zitong Gong and Vladik Kreinovich were visiting researchers at the Leibniz Universität Hannover, supported by the German Research Foundation (DFG), partially under a Mercator Fellowship within the Research Training Group GRK2159 (i.c.sens) and partially under the research project D5 “Risk Assessment of Regeneration Paths for Supporting Simultaneous Decisions” within the Collaborative Research Center (CRC) 871 – Regeneration of Complex Capital Goods.
PY - 2017
Y1 - 2017
N2 - In many practical situations, it is important to know the correlation between different quantities - finding correlations helps to gain insights into various relationships and phenomena, and helps to inform analysts. Often, there is not enough empirical data to experimentally determine all possible correlations. In such cases, a natural idea is to supplement this situation with expert estimates. Expert estimates are rather crude. So, to decide whether to act based on these estimates, it is desirable to know how accurate are expert estimates. In this paper, we propose several techniques for gauging this accuracy.
AB - In many practical situations, it is important to know the correlation between different quantities - finding correlations helps to gain insights into various relationships and phenomena, and helps to inform analysts. Often, there is not enough empirical data to experimentally determine all possible correlations. In such cases, a natural idea is to supplement this situation with expert estimates. Expert estimates are rather crude. So, to decide whether to act based on these estimates, it is desirable to know how accurate are expert estimates. In this paper, we propose several techniques for gauging this accuracy.
KW - correlation
KW - expert estimates
KW - fuzzy sets
KW - uncertainty
KW - vagueness
UR - http://www.scopus.com/inward/record.url?scp=85046081879&partnerID=8YFLogxK
U2 - 10.1109/SSCI.2017.8280790
DO - 10.1109/SSCI.2017.8280790
M3 - Conference contribution
SN - 9781538627273
SP - 1
EP - 9
BT - 2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017
Y2 - 27 November 2017 through 1 December 2017
ER -