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