On the median in imprecise ordinal problems
Author:
Funder
Agence Nationale de la Recherche (FR)
Publisher
Springer Science and Business Media LLC
Subject
Management Science and Operations Research,General Decision Sciences
Link
http://link.springer.com/article/10.1007/s10479-016-2253-x/fulltext.html
Reference29 articles.
1. Ahn, B., & Choi, S. (2012). Aggregation of ordinal data using ordered weighted averaging operator weights. Annals of Operations Research, 201(1), 1–16.
2. Angilella, S., Bottero, M., Corrente, S., Ferretti, V., Greco, S., & Lami, I. M. (2015). Non additive robust ordinal regression for urban and territorial planning: an application for siting an urban waste landfill. Annals of Operations Research, 1–30. doi: 10.1007/s10479-015-1787-7 .
3. Benavoli, A., & Zaffalon, M. (2013). Density-ratio robustness in dynamic state estimation. Mechanical Systems and Signal Processing, 37(1–2), 54–75.
4. Berger, J. O. (1985). Statistical decision theory and Bayesian analysis. Berlin: Springer.
5. Cattaneo, M. E., & Wiencierz, A. (2012). Likelihood-based imprecise regression. International Journal of Approximate Reasoning, 53(8), 1137–1154.
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