TY - GEN
T1 - Computational Procedures for Improving Extreme Value Estimation in Time Series Modelling
AU - Gomes, Dora Prata
AU - Cordeiro, Clara
AU - Neves, Manuela
N1 - Funding Information:
info:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F00006%2F2020/PT#
info:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDP%2F00297%2F2020/PT#
info:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F00297%2F2020/PT#
© 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2023
Y1 - 2023
N2 - In the last decades, some work has been developed in parameter estimation of extreme values jointly with time series analysis. Those results show relevant asymptotic properties. However, for finite samples, limiting results provide approximations that can be poor. Some challenges have been developed by combining Extreme Value Theory and time series modelling to obtain more reliable extreme value parameter estimates. In classical time series modelling a key issue is to determine how many parameters must be included in the model. Special care must be given to extreme events in the series that need specific statistical procedures based on the behaviour of extremes. Resampling procedures such as the jackknife and the bootstrap have been used to improve parameters estimation in Extreme Value Theory combined with time series modelling. New approaches, based on bootstrap procedures are shown and are illustrated with a real data set using the software.
AB - In the last decades, some work has been developed in parameter estimation of extreme values jointly with time series analysis. Those results show relevant asymptotic properties. However, for finite samples, limiting results provide approximations that can be poor. Some challenges have been developed by combining Extreme Value Theory and time series modelling to obtain more reliable extreme value parameter estimates. In classical time series modelling a key issue is to determine how many parameters must be included in the model. Special care must be given to extreme events in the series that need specific statistical procedures based on the behaviour of extremes. Resampling procedures such as the jackknife and the bootstrap have been used to improve parameters estimation in Extreme Value Theory combined with time series modelling. New approaches, based on bootstrap procedures are shown and are illustrated with a real data set using the software.
KW - Extreme value theory
KW - Parameter estimation
KW - Resampling procedures
KW - Time series
UR - http://www.scopus.com/inward/record.url?scp=85168758840&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-37108-0_6
DO - 10.1007/978-3-031-37108-0_6
M3 - Conference contribution
AN - SCOPUS:85168758840
SN - 978-3-031-37107-3
T3 - Lecture Notes in Computer Science
SP - 84
EP - 96
BT - Computational Science and Its Applications – ICCSA 2023 Workshops
A2 - Gervasi, Osvaldo
A2 - Murgante, Beniamino
A2 - Rocha, Ana Maria A. C.
A2 - Garau, Chiara
A2 - Scorza, Francesco
A2 - Karaca, Yeliz
A2 - Torre, Carmelo M.
PB - Springer
CY - Cham
T2 - 23rd International Conference on Computational Science and Its Applications, ICCSA 2023
Y2 - 3 July 2023 through 6 July 2023
ER -