Extreme Value Analysis (EVA) in Python

scikit-extremes is a basic statistical package to perform univariate extreme value calculations using Python

Modelling extreme values

The repo contains the main topics carried out in my master's thesis on operational risk. In particular, it is described how to implement the so called Loss Distribution Approach (LDA), which is considered the state-of-the-art method to compute capital charge among large banks.

Partially-Interpretable Neural Networks for Extreme Value modelling

Threshold Selection and Uncertainty for Extreme Value Analysis

Official implementation of "Extreme Value Meta-Learning for Few-Shot Open-Set Recognition of Hyperspectral Images" (TGRS'23)

Ratio-of-Uniforms Sampling for Bayesian Extreme Value Analysis

CRAN Task View: Extreme Value Analysis

R package to apply the transformed-stationary extreme value analysis

Generalised Additive Extreme Value Models for Location, Scale and Shape

This repository contains code, data, output, and figures associated with the A univariate extreme value analysis and change point detection of monthly discharge in Kali Kupang, Central Java, Indonesia manuscript

Repository for the paper: "Causal Modelling of Heavy-Tailed Variables and Confounders with Application to River Flow".

Likelihood-Based Inference for Time Series Extremes

Python package for fitting statistical models using calibrating priors.

Loglikelihood Adjustment for Extreme Value Models

Extreme value analysis using MATLAB

R-functions for the metastatistical extreme value distribution

Extreme value statistics

Time Series Seasonal Extreme Studentized Deviate(S-ESD) in Python

This repository includes some applications of extreme value analysis techniques for modeling wildfire data. It's a work in progress :) — feel free to reach out if you'd like more details!

A deep study of human longevity using demographic data (HLD, IDL) and Extreme Value Theory to assess the potential existence of a theoretical limit to human lifespan

No description provided.

Python implementation of the paper "A study on wrist identification for forensic investigation" https://www.sciencedirect.com/science/article/abs/pii/S0262885619300733

A demonstration of performing Extreme Value Theory (EVT) using the Block Maxima method with Bayesian sampling in Julia.

Scatter diagrams are typically available in the form of observation counts or normalised into frequencies. These classes of Python modules aim to perform long-term uncertainty modelling of sea state parameters in an automated fashion. The first class takes in the scatter diagram and fits model parameters using DNV recommended probability distributions, while the second class performs various analyses with the fitted model. These include extrapolating return period significant wave heights, and contouring of 𝑁 -year conditions.

Outputs for my thesis. Includes some R codes, mainly on analysis of spatial data

Implementation of the peaks-over-threshold to detect extreme values in time series data

In this project, I will study two dataset: . Fort Collins (Colorado) Daily precipitation amounts (inches) from a single rain gauge in Fort Collins, Colorado / . This dataset shows the annual maximum sea-levels recorded at Port Pirie, a location just north of Adelaide, South Australia, over the period 1923–1987

An exploration of the application of Point Processes to environmental extremes