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Exploiting structure of maximum likelihood estimators for extreme value threshold selection

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

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Exploiting structure of maximum likelihood estimators for extreme value threshold selection. / Wadsworth, Jennifer.
In: Technometrics, Vol. 58, No. 1, 03.2016, p. 116-126.

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

Harvard

Wadsworth, J 2016, 'Exploiting structure of maximum likelihood estimators for extreme value threshold selection', Technometrics, vol. 58, no. 1, pp. 116-126. https://doi.org/10.1080/00401706.2014.998345

APA

Wadsworth, J. (2016). Exploiting structure of maximum likelihood estimators for extreme value threshold selection. Technometrics, 58(1), 116-126. https://doi.org/10.1080/00401706.2014.998345

Vancouver

Wadsworth J. Exploiting structure of maximum likelihood estimators for extreme value threshold selection. Technometrics. 2016 Mar;58(1):116-126. Epub 2015 Jan 22. doi: 10.1080/00401706.2014.998345

Author

Wadsworth, Jennifer. / Exploiting structure of maximum likelihood estimators for extreme value threshold selection. In: Technometrics. 2016 ; Vol. 58, No. 1. pp. 116-126.

Bibtex

@article{8f45c7709cf74bbab1ae6a728f550b45,
title = "Exploiting structure of maximum likelihood estimators for extreme value threshold selection",
abstract = "In order to model the tail of a distribution, one has to define the threshold above or below which an extreme value model produces a suitable fit. Parameter stability plots, whereby one plots maximum likelihood estimates of supposedly threshold-independent parameters against threshold, form one of the main tools for threshold selection by practitioners, principally due to their simplicity. However, one repeated criticism of these plots is their lack of interpretability, with pointwise confidence intervals being strongly dependent across the range of thresholds. In this article, we exploit the independent-increments structure of maximum likelihood estimators in order to produce complementary plots with greater interpretability, and a suggest simple likelihood-based procedure which allows for automated threshold selection.",
keywords = "diagnostic plots, extreme value modelling, maximum likelihood, threshold selection",
author = "Jennifer Wadsworth",
note = "The final, definitive version of this article has been published in the Journal, Technometrics, 58 (1), 2016, {\textcopyright} Informa Plc",
year = "2016",
month = mar,
doi = "10.1080/00401706.2014.998345",
language = "English",
volume = "58",
pages = "116--126",
journal = "Technometrics",
issn = "0040-1706",
publisher = "American Statistical Association",
number = "1",

}

RIS

TY - JOUR

T1 - Exploiting structure of maximum likelihood estimators for extreme value threshold selection

AU - Wadsworth, Jennifer

N1 - The final, definitive version of this article has been published in the Journal, Technometrics, 58 (1), 2016, © Informa Plc

PY - 2016/3

Y1 - 2016/3

N2 - In order to model the tail of a distribution, one has to define the threshold above or below which an extreme value model produces a suitable fit. Parameter stability plots, whereby one plots maximum likelihood estimates of supposedly threshold-independent parameters against threshold, form one of the main tools for threshold selection by practitioners, principally due to their simplicity. However, one repeated criticism of these plots is their lack of interpretability, with pointwise confidence intervals being strongly dependent across the range of thresholds. In this article, we exploit the independent-increments structure of maximum likelihood estimators in order to produce complementary plots with greater interpretability, and a suggest simple likelihood-based procedure which allows for automated threshold selection.

AB - In order to model the tail of a distribution, one has to define the threshold above or below which an extreme value model produces a suitable fit. Parameter stability plots, whereby one plots maximum likelihood estimates of supposedly threshold-independent parameters against threshold, form one of the main tools for threshold selection by practitioners, principally due to their simplicity. However, one repeated criticism of these plots is their lack of interpretability, with pointwise confidence intervals being strongly dependent across the range of thresholds. In this article, we exploit the independent-increments structure of maximum likelihood estimators in order to produce complementary plots with greater interpretability, and a suggest simple likelihood-based procedure which allows for automated threshold selection.

KW - diagnostic plots

KW - extreme value modelling

KW - maximum likelihood

KW - threshold selection

U2 - 10.1080/00401706.2014.998345

DO - 10.1080/00401706.2014.998345

M3 - Journal article

VL - 58

SP - 116

EP - 126

JO - Technometrics

JF - Technometrics

SN - 0040-1706

IS - 1

ER -