Tutorial - applying extreme value theory to characterize food-processing systems

Research output: Contribution to journal › Journal article › Research › peer-review

Standard

Tutorial - applying extreme value theory to characterize food-processing systems. / Skou, Peter Bæk; Holroyd, Stephen E.; van der Berg, Franciscus Winfried J.

In: Journal of Chemometrics, Vol. 31, No. 7, e2896, 2017, p. 1-12.

Research output: Contribution to journal › Journal article › Research › peer-review

Harvard

Skou, PB, Holroyd, SE & van der Berg, FWJ 2017, 'Tutorial - applying extreme value theory to characterize food-processing systems', Journal of Chemometrics, vol. 31, no. 7, e2896, pp. 1-12. https://doi.org/10.1002/cem.2896

APA

Skou, P. B., Holroyd, S. E., & van der Berg, F. W. J. (2017). Tutorial - applying extreme value theory to characterize food-processing systems. Journal of Chemometrics, 31(7), 1-12. [e2896]. https://doi.org/10.1002/cem.2896

Vancouver

Skou PB, Holroyd SE, van der Berg FWJ. Tutorial - applying extreme value theory to characterize food-processing systems. Journal of Chemometrics. 2017;31(7):1-12. e2896. https://doi.org/10.1002/cem.2896

Author

Skou, Peter Bæk ; Holroyd, Stephen E. ; van der Berg, Franciscus Winfried J. / Tutorial - applying extreme value theory to characterize food-processing systems. In: Journal of Chemometrics. 2017 ; Vol. 31, No. 7. pp. 1-12.

Bibtex

@article{2db01b30d0b743c381a3b0d796c3ecdb,
title = "Tutorial - applying extreme value theory to characterize food-processing systems",
abstract = "This tutorial presents extreme value theory (EVT) as an analytical tool in process characterization and shows its potential to describe production performance, eg, across different factories, via reliable estimates of the frequency and scale of extreme events. Two alternative EVT methods are discussed: point over threshold and block maxima. We illustrate the theoretical framework for EVT by process data from two different examples from the food-processing industry. Finally, we discuss limitations, decisions, and possibilities when applying EVT for process data.",
keywords = "Extreme value theory, Food processing, Return-level estimation, Return-time estimation",
author = "Skou, {Peter B{\ae}k} and Holroyd, {Stephen E.} and {van der Berg}, {Franciscus Winfried J}",
year = "2017",
doi = "10.1002/cem.2896",
language = "English",
volume = "31",
pages = "1--12",
journal = "Journal of Chemometrics",
issn = "0886-9383",
publisher = "Wiley",
number = "7",

}

RIS

TY - JOUR

T1 - Tutorial - applying extreme value theory to characterize food-processing systems

AU - Skou, Peter Bæk

AU - Holroyd, Stephen E.

AU - van der Berg, Franciscus Winfried J

PY - 2017

Y1 - 2017

N2 - This tutorial presents extreme value theory (EVT) as an analytical tool in process characterization and shows its potential to describe production performance, eg, across different factories, via reliable estimates of the frequency and scale of extreme events. Two alternative EVT methods are discussed: point over threshold and block maxima. We illustrate the theoretical framework for EVT by process data from two different examples from the food-processing industry. Finally, we discuss limitations, decisions, and possibilities when applying EVT for process data.

AB - This tutorial presents extreme value theory (EVT) as an analytical tool in process characterization and shows its potential to describe production performance, eg, across different factories, via reliable estimates of the frequency and scale of extreme events. Two alternative EVT methods are discussed: point over threshold and block maxima. We illustrate the theoretical framework for EVT by process data from two different examples from the food-processing industry. Finally, we discuss limitations, decisions, and possibilities when applying EVT for process data.

KW - Extreme value theory

KW - Food processing

KW - Return-level estimation

KW - Return-time estimation

U2 - 10.1002/cem.2896

DO - 10.1002/cem.2896

M3 - Journal article

AN - SCOPUS:85019169394

VL - 31

SP - 1

EP - 12

JO - Journal of Chemometrics

JF - Journal of Chemometrics

SN - 0886-9383

IS - 7

M1 - e2896

ER -

ID: 179123779