Open AccessA regression model for extreme events and the presence of bimodality with application to energy generation data
Author(s) -
Vasconcelos Julio Cezar Souza,
Cordeiro Gauss Moutinho,
Ortega Edwin Moises Marcos,
Ribeiro João Gabriel
Publication year - 2021
Publication title -
iet renewable power generation
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.005
H-Index - 76
eISSN - 1752-1424
pISSN - 1752-1416
DOI - 10.1049/rpg2.12043
Subject(s) - outlier , quantile regression , bimodality , extreme value theory , statistics , econometrics , robustness (evolution) , regression , logistic regression , regression analysis , computer science , mathematics , biochemistry , physics , chemistry , quantum mechanics , galaxy , gene
Abstract The application of the theory of extreme values has been growing due to increasing interest in extreme natural events. Many articles on extreme values in data modelling consider unimodal data. This work introduces an appropriate regression for extreme values to detect the presence of bimodality by means of systematic components of two parameters of the odd log‐logistic log‐normal distribution. The global influence is addressed to verify the model robustness and to find possible influential points. Quantile residuals are proposed to detect distribution deficiencies and outliers in the new regression. A real dataset from the electricity generation area is analysed, namely the Santo Antônio Hydroelectric Plant in the state of Rondônia (Brazil), to illustrate the potential of the new regression. The main results indicate that the proposed regression can identify changes in the means and variability of the power generation between extreme events, that is, between the months of June and December.