Attendance absences have a substantial impact on student’s future physical and mental health as well as academic progress. Numerous personal, familial, and social issues are among the causes of student absences. Any kind of absence from school should be minimized. Extremely high rates of student absences may indicate the abrupt commencement of a serious school health crisis or public health crisis, such as the spread of tuberculosis or COVID-19, which provides school health professionals with an early warning. We take the extreme values in absence data as the object and attempt to apply the extreme value theory (EVT) to describe the distribution of extreme values. This study aims to predict extreme instances of student absences. School health professionals can take preventative measures to reduce future excessive absences, according to the predicted results. Five statistical distributions were applied to individually characterize the extreme values. Our findings suggest that EVT is a useful tool for predicting extreme student absences, thereby aiding preventative measures in public health.

Dispersal ecology is a topical discipline that involves understanding and predicting plant community responses to multiple drivers of global change. Propagule movements that entail long-distance dispersal (LDD) events are crucial for plants to reach and colonize suitable sites across fragmented landscapes. Yet, LDD events are extremely rare, and thus, obtaining reliable estimates of the maximum distances that propagules move across and of their frequency has been a long-lasting challenge in plant ecology. Recent advances in dispersal ecology have provided reliable records of dispersal distances, but they remain confined to focal populations, limiting our ability to infer the frequency and actual extent of LDD events across landscapes. In this study, we view LDD events as extreme values of a dispersal function, and we apply statistics of extremes to derive the frequency and extent of LDD events of simulated and empirical data sets. We first briefly explain the rationale behind statistic...

Captions:Figure 1: Phase diagram depicting shape parameter ξ for accepted GEV distribution forER-ER multiplex network as a function of IC inclusion probabilities (pin) in both the layers. Region Bcorresponds to the Weibull. Region A stands for undefined distributions. Size of the network N=100 in eachlayer.Figure 2: (Color online) Distribution of Rmax of SF networks with average degree hki = 4 for various ICinclusion probabilities (pin). Histogram is fitted with normal (blue dotted line) and GEV (red solid line)distributions. Network size N=500.Figure 3: (Color online) Distribution of Rmax of SF networks with average degree hki = 6 for various ICinclusion probabilities (pin). Histogram is fitted with normal (blue dotted line) and GEV (red solid line)distributions. Network size N=500.Table 1: Estimated parameters of KS test for fitting GEV and normal distributions of Rmax for differentnetwork sizes of SF network over a average of 5000 random realization. Other parameters are inhibitioninclusion probability pin = 0.5 and average degree k = 6.Table 2: Estimated parameters of KS test for fitting of GEV and normal distributions of Rmax for different inhibitoryinclusion probability (pin) of SF- SF network over 5000 population. Other parameters are network size N = 100 in each layer andaverage degree k = 6.Table 3: Estimated parameters of KS test for fitting of GEV and normal distributions of Rmax for different inhibitoryinclusion probability (pin) of ER- SF network over 5000 population. Other parameters are network size N = 100 in each layer andaverage degree k = 6.

Extreme value analysis of CCHaPS, the Coupled Coastal Hazard Prediction System, which is a 2D hydrodynamic and wave model (Semi-implicit Cross-scale Hydroscience Integrated System Model and Wind Wave Model III, SCHISM-WWMIII), configured around the Australian coastline including the Great Barrier Reef, and extending out to deep offshore waters or neighbouring landmasses (https://data.csiro.au/collection/csiro:65669).

The model simulates water levels due to astronomical tides, weather, waves and aspects of wave-flow interaction over multiple decades, allowing for full consideration of the dynamics of extreme sea levels and waves in the Australian region, at high spatial resolution from ~7 km offshore down to ~250 m at the coast, and ~100 m in major river mouths. The model is run on an unstructured (triangular) grid comprising over 1.4 million nodes, which extends overland up to a 12 m elevation contour, to enable modelling of inundation events and sea level rise.

The CCHaPS hindcast covers the 40-year period 1981 to 2020.

Extreme value analysis has been performed on 1) annual maximum{yr_max} significant wave heights {hs}, and 2) detrended annual maximum {yr_max-detrended} water levels {zos}, with the linear trend in mean water levels removed.

Three types of extreme value distributions (EVDs) are fitted to the annual maxima: - two-parameter Gumbel - three-parameter Generalised Extreme Value (GEV) - four-parameter mixed Gumbel distribution (https://doi.org/10.1038/s41598-022-08382-y)

Datasets are provided for 1) the commonly used GEV {fgev}, and 2) the best of the three EVD {bestEVD} types (with positive shape parameters), selected using the Akaike Information Criterion (AIC). Lower values of AIC indicate better models, in the sense that the model fit is better relative to the number of model parameters. Note: GEV fits with a negative shape parameter (i.e. bounded upper tail) can yield lower estimated extremes and are therefore not always preferred (e.g. Haigh et al., 2014; https://doi.org/10.1007/s00382-012-1652-1).

The dataset comprises 1, 2, 5, 10, 20, and 63% Annual Exceedance Probabilities (AEPs) with {upper} and {lower} 95% confidence intervals, stored as netCDF data on an unstructured grid, compliant with CF, ACDD and UGRID metadata conventions. Additional processing is also applied to the confidence intervals. A notebook demonstrating how to interact with the data will be attached under Supporting Documentation, and also available via GitHub (see Related Links, LINK TBA).

The netCDF files can be viewed in QGIS by importing them as a mesh through the Data Source Manager. (https://docs.qgis.org/3.40/en/docs/user_manual/working_with_mesh/mesh.html?utm_source=chatgpt.com)

The data is also stored on CSIRO infrastructure in the {ev-acs-wp3-cchaps} volume, and at NCI in /g/data/ia39/WP3/release/CCHaPS.

A manuscript describing this dataset is in preparation and will be linked to this metadata record in due course. Lineage: Extreme value analysis of hourly CCHaPS data (https://data.csiro.au/collection/csiro:65669) was performed using the R CRAN package loopevd (https://doi.org/10.32614/CRAN.package.loopevd), June 2025. File converted to metadata standards compliant netCDF via Python notebook, June 2025. Final dataset copied from NCI to CSIRO Bowen storage in preparation for publication via CSIRO DAP.

There is no scientific consensus on the fundamental question whether the probability distribution of the human life span has a finite endpoint or not and, if so, whether this upper limit changes over time. Our study uses a unique dataset of the ages at death - in days - of all (about 285,000) Dutch residents, born in the Netherlands, who died in the years 1986-2015 at a minimum age of 92 years and is based on extreme value theory, the coherent approach to research problems of this type. Unlike some other studies we base our analysis on the configuration of thousands of mortality data of old people, not just the few oldest old. We find compelling statistical evidence that there is indeed an upper limit to the life span of men and to that of women for all the 30 years we consider and, moreover, that there are no indications of trends in these upper limits over the last 30 years, despite the fact that the number of people reaching high age (say 95 years) was almost tripling. We also present estimates for the endpoints, for the force of mortality at very high age, and for the so-called perseverance parameter.

Yearly citation counts for the publication titled "Extreme Value Statistics in Semi-Supervised Models".

Includes extreme significant wave heights, in meters, computed using peaks over threshold method on NOAA 30-year WWIII hindcast data. Extreme values for 5, 10, 25, 50, and 100 year return periods were computed. Sites cover US coastlines. Column 1: Site identifier/name Column 2: Site latitude Column 3: Site longitude Column 4: 5-year extreme Hs, in meters Column 5: 10-year extreme Hs, in meters Column 6: 25-year extreme Hs, in meters Column 7: 50-year extreme Hs, in meters Column 8: 100-year extreme Hs, in meters

This study is a first global estimate of the extreme water level values along the metropolitan coastline. It is to be refined locally with all available data and knowledge. The method used is based on a statistical analysis of the tide charts available in ports. It does not take into account the observations of the waves. The results between ports are obtained by an interpolation method. The study produces statistical estimates at reference ports: — extreme values of open sea overcots in the Channel and Atlantic; — extreme water level values for the entire metropolis. And a set of statistical estimation maps of extreme water levels along the coastline. The estimates provided go up to the return period 1000 years. In view of the observation times used at ports, the user must check whether the estimates of a return period of more than 50 or 100 years still make sense.

Attendance absences have a substantial impact on student’s future physical and mental health as well as academic progress. Numerous personal, familial, and social issues are among the causes of student absences. Any kind of absence from school should be minimized. Extremely high rates of student absences may indicate the abrupt commencement of a serious school health crisis or public health crisis, such as the spread of tuberculosis or COVID-19, which provides school health professionals with an early warning. We take the extreme values in absence data as the object and attempt to apply the extreme value theory (EVT) to describe the distribution of extreme values. This study aims to predict extreme instances of student absences. School health professionals can take preventative measures to reduce future excessive absences, according to the predicted results. Five statistical distributions were applied to individually characterize the extreme values. Our findings suggest that EVT is a useful tool for predicting extreme student absences, thereby aiding preventative measures in public health.

Central ideas from thermal biology, including thermal performance curves and tolerances, have been widely used to evaluate how changes in environmental means and variances generate changes in fitness, selection and microevolution in response to climate change. We summarize the opportunities and challenges for extending this approach to understanding the consequences of extreme climatic events. Using statistical tools from extreme value theory, we show how distributions of thermal extremes vary with latitude, time scale and climate change. Second, we review how performance curves and tolerances have been used to predict the fitness and evolutionary responses to climate change and climate gradients. Performance curves and tolerances change with prior thermal history and with time scale, complicating their use for predicting responses to thermal extremes. Third, we describe several recent case studies showing how infrequent extreme events can have outsized effects on the evolution of performance curves and heat tolerance. A key issue is whether thermal extremes affect reproduction or survival, and how these combine to determine overall fitness. We argue that a greater focus on tails—in the distribution of environmental extremes, and in the upper ends of performance curves—is needed to understand the consequences of extreme events.

Sweden Consumer Survey: KI: Perceived Inflation Now: excl Extreme Values data was reported at 2.210 % in Jul 2018. This records a decrease from the previous number of 2.550 % for Jun 2018. Sweden Consumer Survey: KI: Perceived Inflation Now: excl Extreme Values data is updated monthly, averaging 1.720 % from Dec 2001 (Median) to Jul 2018, with 200 observations. The data reached an all-time high of 4.770 % in Jul 2008 and a record low of 0.000 % in Mar 2016. Sweden Consumer Survey: KI: Perceived Inflation Now: excl Extreme Values data remains active status in CEIC and is reported by National Institute of Economic Research. The data is categorized under Global Database’s Sweden – Table SE.H009: Consumer Survey: National Institute of Economic Research.

The file mortality 1986-2015 selection' contains the raw data.Relevant are the columns: C born in The Netherlands or abroad D gender F year of death G age at death in days. The filesages 1986-2015 - women' and `ages 1986-2015 - men' present the data in a form more suitable for our project. In each file, the 30 columns (A-AD) contain the relevant ages of death in days for the years of death 1986-2015, for women and for men respectively.

In this paper, we study a credit risk (collateral) management scheme for the Canadian retail payment system designed to cover the exposure of a defaulting member. We estimate ex ante the size of a collateral pool large enough to cover exposure for a historical worst-case default scenario. The parameters of the distribution of the maxima are estimated using two main statistical approaches based on extreme value models: Block-Maxima for different window lengths (daily, weekly and monthly) and Peak-over-Threshold. Our statistical model implies that the largest daily net debit position across participants exceeds roughly $1.5 billion once a year. Despite relying on extreme-value theory, the out of sample forecasts may still underestimate an actual exposure given the absence of observed data on defaults and financial stress in Canada. Our results are informative for optimal collateral management and system design of pre-funded retail-payment schemes.

We propose a computational model of vision that describes the integration of cross-modal sensory information between the olfactory and visual systems in zebrafish based on the principles of the statistical extreme value theory. The integration of olfacto-retinal information is mediated by the centrifugal pathway that originates from the olfactory bulb and terminates in the neural retina. Motivation for using extreme value theory stems from physiological evidence suggesting that extremes and not the mean of the cell responses direct cellular activity in the vertebrate brain. We argue that the visual system, as measured by retinal ganglion cell responses in spikes/sec, follows an extreme value process for sensory integration and the increase in visual sensitivity from the olfactory input can be better modeled using extreme value distributions. As zebrafish maintains high evolutionary proximity to mammals, our model can be extended to other vertebrates as well.

Sweden Inflation Expectation: KI: Household: 12 Months Ahead: incl Extreme Values data was reported at 3.000 % in Oct 2018. This records an increase from the previous number of 2.800 % for Sep 2018. Sweden Inflation Expectation: KI: Household: 12 Months Ahead: incl Extreme Values data is updated monthly, averaging 2.400 % from Dec 2001 (Median) to Oct 2018, with 203 observations. The data reached an all-time high of 5.200 % in Jul 2008 and a record low of 0.600 % in Mar 2005. Sweden Inflation Expectation: KI: Household: 12 Months Ahead: incl Extreme Values data remains active status in CEIC and is reported by National Institute of Economic Research. The data is categorized under Global Database’s Sweden – Table SE.I031: Inflation Expectation: National Institute of Economic Research .

Monthly three-day maximal number of stranded individualsThe published data are the necessary data to redo the analysis used in the targeted publication. For each species, the data are the three-day maximal number of stranded individuals for each month and over the period from 1990 to 2016 (for the short-beaked common dolphin and the striped dolphin) or the period from 1997 to 2016 (for the harbour porpoise). Please refer to the targeted publication and the Supporting Information.Data.zip

In the analysis of DNA sequences on related individuals, most methods strive to incorporate as much information as possible, with little or no attention paid to the issue of statistical significance. For example, a modern workstation can easily handle the computations needed to perform a large-scale genome-wide inheritance-by-descent (IBD) scan, but accurate assessment of the significance of that scan is often hindered by inaccurate approximations and computationally intensive simulation. To address these issues, we developed gLOD-a test of co-segregation that, for large samples, models chromosome-specific IBD statistics as a collection of stationary Gaussian processes. With this simple model, the parametric bootstrap yields an accurate and rapid assessment of significance-the genome-wide corrected P-value. Furthermore, we show that (i) under the null hypothesis, the limiting distribution of the gLOD is the standard Gumbel distribution; (ii) our parametric bootstrap simulator is approxi...

This dataset is used as input for implementing a new methodology to estimate Probable Maximum Precipitation (PMP), which is based on the Generalized Extreme Value (GEV) distribution and regionalization analysis. It consists of 428 time series of annual maximum daily precipitation (AMDP) recorded across Chile, along with the corresponding metadata.

This dataset mainly describes the annual water level statistical information of various groundwater wells under the Water Resources Agency, including the annual average water level of each well, the annual highest daily water level, the date of occurrence of the annual highest daily water level, the annual lowest daily water level, the date of occurrence of the annual lowest daily water level, the momentary highest water level, the date and time of occurrence of the momentary highest water level, the momentary lowest water level, and the date and time of occurrence of the momentary lowest water level. It helps to understand the extreme values of water levels in each observation well each year, and this dataset can be used to grasp the trend of the extreme value changes of observation wells over the years. The production of this dataset involves the monthly collection of observation information of observation wells by the first to tenth river basin offices under the Water Resources Agency, and is derived through statistical calculation.

This data was prepared as input for the Selkie GIS-TE tool. This GIS tool aids site selection, logistics optimization and financial analysis of wave or tidal farms in the Irish and Welsh maritime areas. Read more here: https://www.selkie-project.eu/selkie-tools-gis-technoeconomic-model/

This research was funded by the Science Foundation Ireland (SFI) through MaREI, the SFI Research Centre for Energy, Climate and the Marine and by the Sustainable Energy Authority of Ireland (SEAI). Support was also received from the European Union's European Regional Development Fund through the Ireland Wales Cooperation Programme as part of the Selkie project.

File Formats

Results are presented in three file formats:

tif Can be imported into a GIS software (such as ARC GIS) csv Human-readable text format, which can also be opened in Excel png Image files that can be viewed in standard desktop software and give a spatial view of results

Input Data

All calculations use open-source data from the Copernicus store and the open-source software Python. The Python xarray library is used to read the data.

Hourly Data from 2000 to 2019

• Wind - Copernicus ERA5 dataset 17 by 27.5 km grid
  10m wind speed

• Wave - Copernicus Atlantic -Iberian Biscay Irish - Ocean Wave Reanalysis dataset 3 by 5 km grid

Accessibility

The maximum limits for Hs and wind speed are applied when mapping the accessibility of a site.
The Accessibility layer shows the percentage of time the Hs (Atlantic -Iberian Biscay Irish - Ocean Wave Reanalysis) and wind speed (ERA5) are below these limits for the month.

Input data is 20 years of hourly wave and wind data from 2000 to 2019, partitioned by month. At each timestep, the accessibility of the site was determined by checking if
the Hs and wind speed were below their respective limits. The percentage accessibility is the number of hours within limits divided by the total number of hours for the month.

Environmental data is from the Copernicus data store (https://cds.climate.copernicus.eu/). Wave hourly data is from the 'Atlantic -Iberian Biscay Irish - Ocean Wave Reanalysis' dataset.
Wind hourly data is from the ERA 5 dataset.

Availability

A device's availability to produce electricity depends on the device's reliability and the time to repair any failures. The repair time depends on weather
windows and other logistical factors (for example, the availability of repair vessels and personnel.). A 2013 study by O'Connor et al. determined the
relationship between the accessibility and availability of a wave energy device. The resulting graph (see Fig. 1 of their paper) shows the correlation between accessibility at Hs of 2m and wind speed of 15.0m/s and availability. This graph is used to calculate the availability layer from the accessibility layer.

The input value, accessibility, measures how accessible a site is for installation or operation and maintenance activities. It is the percentage time the
environmental conditions, i.e. the Hs (Atlantic -Iberian Biscay Irish - Ocean Wave Reanalysis) and wind speed (ERA5), are below operational limits.
Input data is 20 years of hourly wave and wind data from 2000 to 2019, partitioned by month. At each timestep, the accessibility of the site was determined
by checking if the Hs and wind speed were below their respective limits. The percentage accessibility is the number of hours within limits divided by the total
number of hours for the month. Once the accessibility was known, the percentage availability was calculated using the O'Connor et al. graph of the relationship between the two. A mature technology reliability was assumed.

Weather Window

The weather window availability is the percentage of possible x-duration windows where weather conditions (Hs, wind speed) are below maximum limits for the
given duration for the month.

The resolution of the wave dataset (0.05° × 0.05°) is higher than that of the wind dataset
(0.25° x 0.25°), so the nearest wind value is used for each wave data point. The weather window layer is at the resolution of the wave layer.

The first step in calculating the weather window for a particular set of inputs (Hs, wind speed and duration) is to calculate the accessibility at each timestep.
The accessibility is based on a simple boolean evaluation: are the wave and wind conditions within the required limits at the given timestep?

Once the time series of accessibility is calculated, the next step is to look for periods of sustained favourable environmental conditions, i.e. the weather
windows. Here all possible operating periods with a duration matching the required weather-window value are assessed to see if the weather conditions remain
suitable for the entire period. The percentage availability of the weather window is calculated based on the percentage of x-duration windows with suitable
weather conditions for their entire duration.The weather window availability can be considered as the probability of having the required weather window available
at any given point in the month.

Extreme Wind and Wave

The Extreme wave layers show the highest significant wave height expected to occur during the given return period. The Extreme wind layers show the highest wind speed expected to occur during the given return period.

To predict extreme values, we use Extreme Value Analysis (EVA). EVA focuses on the extreme part of the data and seeks to determine a model to fit this reduced
portion accurately. EVA consists of three main stages. The first stage is the selection of extreme values from a time series. The next step is to fit a model
that best approximates the selected extremes by determining the shape parameters for a suitable probability distribution. The model then predicts extreme values
for the selected return period. All calculations use the python pyextremes library. Two methods are used - Block Maxima and Peaks over threshold.

The Block Maxima methods selects the annual maxima and fits a GEVD probability distribution.

The peaks_over_threshold method has two variable calculation parameters. The first is the percentile above which values must be to be selected as extreme (0.9 or 0.998). The second input is the time difference between extreme values for them to be considered independent (3 days). A Generalised Pareto Distribution is fitted to the selected
extremes and used to calculate the extreme value for the selected return period.