Extreme value modeling with errors-in-variables in detection and attribution of changes in climate extremes

• Published in: Statistics and computing Vol. 33; no. 6
• Main Authors: Lau, Yuen Tsz Abby, Wang, Tianying, Yan, Jun, Zhang, Xuebin
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.12.2023; Springer Nature B.V
• Subjects: Artificial Intelligence; Climate change; Computer Science; Error analysis; Extreme values; Modelling; Original Paper; Probability and Statistics in Computer Science; Statistical Theory and Methods; Statistics and Computing/Statistics Programs; Corrected score; Estimating equation; Monte Carlo; Measurement error

The generalized extreme value (GEV) regression provides a framework for modeling extreme events across various fields by incorporating covariates into the location parameter of GEV distributions. When the covariates are subject to errors-in-variables (EIV) or measurement error, ignoring the EIVs lea...