Threshold selection in univariate extreme value analysis

Schneider, Laura Fee; Krajina, Andrea; Krivobokova, Tatyana

doi:10.1007/s10687-021-00405-7

Cited by 11 publications

(4 citation statements)

References 40 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Recently, new methods have been developed to automate some of the existing proposals, especially those based on visual data inspection, see for instance Wu and Qiu (2018), Bader et al (2018), Caballero-Megido et al (2018 and Queensley et al (2019), Schneider et al (2021) among others. Wu and Qiu (2018) propose a method to select the suitable threshold based on multiple criteria decision analysis (MCDA).…”

Section: Ii) Numerical Approachesmentioning

confidence: 99%

“…The method incorporates the visual technique and is aimed at reducing the subjectivity associated with solely using the eye inspection approach (EIA). Schneider et al (2021) suggest a couple of automated methods for threshold selection. The first one consists in estimating and minimizing the integrated square error (ISE) between the exponential density and its parametric estimator employing the Hill estimator.…”

Section: Ii) Numerical Approachesmentioning

confidence: 99%

See 1 more Smart Citation

Assessing the importance of the choice threshold in quantifying market risk under the POT approach (EVT)

2023

View full text Add to dashboard Cite

From a theoretical point of view, the selection of thresholds is a critical issue in the framework of the Peaks Over Threshold (POT) approach, which is why in the last decade numerous methodologies have been proposed for its selection. In this paper, we address this subject from an empirical point of view by assessing to what extent the selection of the threshold is decisive in quantifying the market risk. For measuring market risk, we use the Value at Risk (VaR) and the Expected Shortfall (ES) measures. The results obtained indicate that there is a large set of thresholds that provide similar Generalized Pareto Distribution (GPD) quantiles estimators and as a consequence similar market risk measures. Just only, for large thresholds, those corresponding to the 98th and 99th percentile of the GPD some differences are found. It means that the choice of threshold in the framework of the POT method may not be relevant in quantifying market risk when we use the VaR and ES measures for this task.

show abstract

Section: Ii) Numerical Approachesmentioning

confidence: 99%

Section: Ii) Numerical Approachesmentioning

confidence: 99%

Assessing the importance of the choice threshold in quantifying market risk under the POT approach (EVT)

2023

View full text Add to dashboard Cite

show abstract

“…For example, a low threshold increases the number of values in the POT series but compromises the independence of data and affects trend estimates (e.g., Markonis et al., 2019). A universally accepted threshold‐selection method does not exist (Durocher et al., 2018) and several techniques have been suggested in the literature (e.g., Deidda, 2010; DuMouchel, 1983; Dupuis, 1999; Ferreira et al., 2003; Liang et al., 2019; X. Pan et al., 2022; Schneider et al., 2021; Solari & Losada, 2012; Thompson et al., 2009). Many studies used the so‐called Expert Team on Climate Change Detection Monitoring and Indices (ETCCDI; WMO et al., 2009) to estimate extreme temperature trends (e.g., Alexander et al., 2006; Rajulapati et al., 2022; P. Zhang et al., 2019).…”

Section: Introductionmentioning

confidence: 99%

Nonstationarity in High and Low‐Temperature Extremes: Insights From a Global Observational Data Set by Merging Extreme‐Value Methods

Nerantzaki,

Papalexiou,

Rajulapati

et al. 2023

Earth's Future

View full text Add to dashboard Cite

We merge classical extreme value methods to extract high (high temperatures (HT)) and low (low temperatures (LT)) temperatures and form time series having at least one extreme value per year. Observed daily maximum and minimum temperature records are used from 4,797 quality‐controlled, global, surface stations over 1970–2019. We assess changes in the magnitude and frequency of extreme temperatures by introducing and applying novel methods that exploit the definition of stationarity. Analysis shows significant increasing (40.6% of the stations) and decreasing (41.1%) trends in the frequency of high and LT, respectively, and increasing trends in both high‐ and low‐temperature values (35.6% and 49.7%). Globally, HT and LT frequencies are increasing and decreasing, respectively, by 0.9% and 1.1% per year, relative to the expected frequencies under the assumption of stationarity. The global mean annual HT and LT magnitudes are increasing by 0.004 and 0.016°C/year compared to the expected ones under stationarity. The results indicate that the assumption of stationarity fails to explain the observed changes. The proposed methods are an alternative approach to classical extreme value methods and a useful tool to reveal changes in extremes in the era of earth‐system change.

show abstract

“…The choice of q in the first step is a standard issue in extreme value theory [55][56][57]. For theoretical results, q should be growing with the sample size; that is, q = q n satisfying lim n→∞ q n = 1 and lim n→∞ n(1 − q n ) = ∞.…”

mentioning

confidence: 99%

Extreme Treatment Effect: Extrapolating Dose-Response Function into Extreme Treatment Domain

Bodik

2024

Mathematics

View full text Add to dashboard Cite

The potential outcomes framework serves as a fundamental tool for quantifying causal effects. The average dose–response function μ(t) (also called the effect curve) is typically of interest when dealing with a continuous treatment variable (exposure). The focus of this work is to determine the impact of an extreme level of treatment, potentially beyond the range of observed values—that is, estimating μ(t) for very large t. Our approach is grounded in the field of statistics known as extreme value theory. We outline key assumptions for the identifiability of the extreme treatment effect. Additionally, we present a novel and consistent estimation procedure that can potentially reduce the dimension of the confounders to at most 3. This is a significant result since typically, the estimation of μ(t) is very challenging due to high-dimensional confounders. In practical applications, our framework proves valuable when assessing the effects of scenarios such as drug overdoses, extreme river discharges, or extremely high temperatures on a variable of interest.

show abstract

Threshold selection in univariate extreme value analysis

Cited by 11 publications

References 40 publications

Assessing the importance of the choice threshold in quantifying market risk under the POT approach (EVT)

Assessing the importance of the choice threshold in quantifying market risk under the POT approach (EVT)

Nonstationarity in High and Low‐Temperature Extremes: Insights From a Global Observational Data Set by Merging Extreme‐Value Methods

Extreme Treatment Effect: Extrapolating Dose-Response Function into Extreme Treatment Domain

Contact Info

Product

Resources

About