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  • SuperQuantile Regression

    Rights statement: This is an Accepted Manuscript of an article published by Taylor & Francis in Journal of Applied Statistics on 20/03/2018, available online: http://www.tandfonline.com/10.1080/02664763.2018.1450363

    Accepted author manuscript, 672 KB, PDF document

    Available under license: CC BY-NC: Creative Commons Attribution-NonCommercial 4.0 International License

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Bayesian CV@R/super-quantile regression

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Bayesian CV@R/super-quantile regression. / Tsionas, Efthymios; Izzeldin, Marwan.
In: Journal of Applied Statistics, 20.03.2018.

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Harvard

Tsionas, E & Izzeldin, M 2018, 'Bayesian CV@R/super-quantile regression', Journal of Applied Statistics. https://doi.org/10.1080/02664763.2018.1450363

APA

Tsionas, E., & Izzeldin, M. (2018). Bayesian CV@R/super-quantile regression. Journal of Applied Statistics. Advance online publication. https://doi.org/10.1080/02664763.2018.1450363

Vancouver

Tsionas E, Izzeldin M. Bayesian CV@R/super-quantile regression. Journal of Applied Statistics. 2018 Mar 20. Epub 2018 Mar 20. doi: 10.1080/02664763.2018.1450363

Author

Tsionas, Efthymios ; Izzeldin, Marwan. / Bayesian CV@R/super-quantile regression. In: Journal of Applied Statistics. 2018.

Bibtex

@article{7d3d777d05f94e24b9f08a210bb99137,
title = "Bayesian CV@R/super-quantile regression",
abstract = "In this paper we provide a Bayesian interpretation of the conditional value at risk, CV@R, or super-quantile regression recently developed by Rockafellar et al. [Super-quantile regression with applications to buffered reliability, uncertainty quantification, and conditional value-at-risk, Eur. J. Oper. Res. 234 (2014), pp. 140–154]. Computations are based on particle filtering using a special posterior distribution consistent with the super-quantile concept. An empirical application to data used by RRM as well to another data set on energy prices confirms their results and shows the applicability of the new techniques.",
keywords = "CV@R, super-quantile regression, risk measures, Bayesian analysis, particle filtering",
author = "Efthymios Tsionas and Marwan Izzeldin",
note = "This is an Accepted Manuscript of an article published by Taylor & Francis in Journal of Applied Statistics on 20/03/2018, available online: http://www.tandfonline.com/10.1080/02664763.2018.1450363",
year = "2018",
month = mar,
day = "20",
doi = "10.1080/02664763.2018.1450363",
language = "English",
journal = "Journal of Applied Statistics",
issn = "0266-4763",
publisher = "Routledge",

}

RIS

TY - JOUR

T1 - Bayesian CV@R/super-quantile regression

AU - Tsionas, Efthymios

AU - Izzeldin, Marwan

N1 - This is an Accepted Manuscript of an article published by Taylor & Francis in Journal of Applied Statistics on 20/03/2018, available online: http://www.tandfonline.com/10.1080/02664763.2018.1450363

PY - 2018/3/20

Y1 - 2018/3/20

N2 - In this paper we provide a Bayesian interpretation of the conditional value at risk, CV@R, or super-quantile regression recently developed by Rockafellar et al. [Super-quantile regression with applications to buffered reliability, uncertainty quantification, and conditional value-at-risk, Eur. J. Oper. Res. 234 (2014), pp. 140–154]. Computations are based on particle filtering using a special posterior distribution consistent with the super-quantile concept. An empirical application to data used by RRM as well to another data set on energy prices confirms their results and shows the applicability of the new techniques.

AB - In this paper we provide a Bayesian interpretation of the conditional value at risk, CV@R, or super-quantile regression recently developed by Rockafellar et al. [Super-quantile regression with applications to buffered reliability, uncertainty quantification, and conditional value-at-risk, Eur. J. Oper. Res. 234 (2014), pp. 140–154]. Computations are based on particle filtering using a special posterior distribution consistent with the super-quantile concept. An empirical application to data used by RRM as well to another data set on energy prices confirms their results and shows the applicability of the new techniques.

KW - CV@R

KW - super-quantile regression

KW - risk measures

KW - Bayesian analysis

KW - particle filtering

U2 - 10.1080/02664763.2018.1450363

DO - 10.1080/02664763.2018.1450363

M3 - Journal article

JO - Journal of Applied Statistics

JF - Journal of Applied Statistics

SN - 0266-4763

ER -