Relevant bibliographies by topics / Superquantiles

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers
  5. Reports

Academic literature on the topic 'Superquantiles'

Author: Grafiati
Published: 7 December 2024
Last updated: 31 July 2025

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Journal articles on the topic "Superquantiles"

1

Rio, Emmanuel. "Upper bounds for superquantiles of martingales." Comptes Rendus. Mathématique 359, no. 7 (2021): 813–22. http://dx.doi.org/10.5802/crmath.207.

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D, Coulibaly, Badiane Sihintoe, Kalivogui Siba, and Ghizlane Chaibi. "A Comprehensive Examination of CVaR and bPOE in Common Probability Distributions: Applications in Portfolio Optimization and Density Estimation." American Journal of Information Science and Technology 9, no. 2 (2025): 111–27. https://doi.org/10.11648/j.ajist.20250902.15.

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This study examines portfolio risk assessment in finance using advanced quantile and superquantile techniques. The portfolio analyzed consists of widely recognized stocks, including Apple (AAPL), Microsoft (MSFT), Alphabet (GOOGL), and Tesla (TSLA). The primary objective is to enhance the accuracy and robustness of financial risk evaluation for such a portfolio. To achieve this, we developed innovative methods for computing quantiles and superquantiles, leveraging various probability distributions. In particular, we explored the Exponential, Gumbel, Frchet, and α-stable distributions to model
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3

Laguel, Yassine, Krishna Pillutla, Jérôme Malick, and Zaid Harchaoui. "Superquantiles at Work: Machine Learning Applications and Efficient Subgradient Computation." Set-Valued and Variational Analysis 29, no. 4 (2021): 967–96. http://dx.doi.org/10.1007/s11228-021-00609-w.

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4

Kala, Zdeněk. "Global Sensitivity Analysis of Quantiles: New Importance Measure Based on Superquantiles and Subquantiles." Symmetry 13, no. 2 (2021): 263. http://dx.doi.org/10.3390/sym13020263.

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The article introduces quantile deviation l as a new sensitivity measure based on the difference between superquantile and subquantile. New global sensitivity indices based on the square of l are presented. The proposed sensitivity indices are compared with quantile-oriented sensitivity indices subordinated to contrasts and classical Sobol sensitivity indices. The comparison is performed in a case study using a non-linear mathematical function, the output of which represents the elastic resistance of a slender steel member under compression. The steel member has random imperfections that reduc
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Dedecker, Jérôme, and Florence Merlevède. "Central limit theorem and almost sure results for the empirical estimator of superquantiles/CVaR in the stationary case." Statistics 56, no. 1 (2022): 53–72. http://dx.doi.org/10.1080/02331888.2022.2043325.

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Mafusalov, Alexander, and Stan Uryasev. "CVaR (superquantile) norm: Stochastic case." European Journal of Operational Research 249, no. 1 (2016): 200–208. http://dx.doi.org/10.1016/j.ejor.2015.09.058.

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7

Rockafellar, R. Tyrrell, and Johannes O. Royset. "Superquantile/CVaR risk measures: second-order theory." Annals of Operations Research 262, no. 1 (2016): 3–28. http://dx.doi.org/10.1007/s10479-016-2129-0.

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Laguel, Yassine, Jérôme Malick, and Zaid Harchaoui. "Superquantile-Based Learning: A Direct Approach Using Gradient-Based Optimization." Journal of Signal Processing Systems 94, no. 2 (2022): 161–77. http://dx.doi.org/10.1007/s11265-021-01716-5.

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Nair, N. Unnikrishnan, S. M. Sunoj, and Silpa Subhash. "Superquantile function of order n and their applications in reliability and entropy." Statistics & Probability Letters 225 (October 2025): 110457. https://doi.org/10.1016/j.spl.2025.110457.

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10

Rockafellar, R. T., J. O. Royset, and S. I. Miranda. "Superquantile regression with applications to buffered reliability, uncertainty quantification, and conditional value-at-risk." European Journal of Operational Research 234, no. 1 (2014): 140–54. http://dx.doi.org/10.1016/j.ejor.2013.10.046.

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Dissertations / Theses on the topic "Superquantiles"

1

Thurin, Gauthier. "Quantiles multivariés et transport optimal régularisé." Electronic Thesis or Diss., Bordeaux, 2024. http://www.theses.fr/2024BORD0262.

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L’objet d’intérêt principal de cette thèse est la fonction quantile de Monge- Kantorovich. On s’intéresse d’abord à la question cruciale de son estimation, qui revient à résoudre un problème de transport optimal. En particulier, on tente de tirer profit de la connaissance a priori de la loi de référence, une information additionnelle par rapport aux algorithmes usuels, qui nous permet de paramétrer les potentiels de transport par leur série de Fourier. Ce faisant, la régularisation entropique du transport optimal permet deux avantages : la construction d’un algorithme efficace et convergent po
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Miranda, Sofia I. "Superquantile regression: theory, algorithms, and applications." Thesis, Monterey, California: Naval Postgraduate School, 2014. http://hdl.handle.net/10945/44618.

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Approved for public release; distribution is unlimited<br>We present a novel regression framework centered on a coherent and averse measure of risk, the superquantile risk (also called conditional value-at-risk), which yields more conservatively fitted curves than classical least squares and quantile regressions. In contracts to other generalized regression techniques that approximate conditional superquantiles by various combinations of conditional quantiles, we directly and inperfect analog to classical regressional obtain superquantile regression functions as optimal solutions of certain
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Book chapters on the topic "Superquantiles"

1

Miranda, Sofia Isabel. "Applying Superquantile Regression to a Real-World Problem: Submariners Effort Index Analysis." In Studies in Big Data. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-24154-8_14.

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Rockafellar, R. Tyrrell, and Johannes O. Royset. "Superquantiles and Their Applications to Risk, Random Variables, and Regression." In Theory Driven by Influential Applications. INFORMS, 2013. http://dx.doi.org/10.1287/educ.2013.0111.

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Conference papers on the topic "Superquantiles"

1

Laguel, Yassine, Jerome Malick, and Zaid Harchaoui. "First-Order Optimization for Superquantile-Based Supervised Learning." In 2020 IEEE 30th International Workshop on Machine Learning for Signal Processing (MLSP). IEEE, 2020. http://dx.doi.org/10.1109/mlsp49062.2020.9231909.

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Laguel, Yassine, Krishna Pillutla, Jerome Malick, and Zaid Harchaoui. "A Superquantile Approach to Federated Learning with Heterogeneous Devices." In 2021 55th Annual Conference on Information Sciences and Systems (CISS). IEEE, 2021. http://dx.doi.org/10.1109/ciss50987.2021.9400318.

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Reports on the topic "Superquantiles"

1

Rockafellar, R. T., and Johannes O. Royset. Superquantile/CVaR Risk Measures: Second-Order Theory. Defense Technical Information Center, 2014. http://dx.doi.org/10.21236/ada615948.

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Rockafellar, R. T., and Johannes O. Royset. Superquantile/CVaR Risk Measures: Second-Order Theory. Defense Technical Information Center, 2015. http://dx.doi.org/10.21236/ada627217.

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Journal articles
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