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

Simple item page
dc.contributor.authorLau, Yuen Tsz Abby
dc.contributor.authorWang, Tianying
dc.contributor.authorYan, Jun
dc.contributor.authorZhang, Xuebin
dc.date.accessioned2025-04-10T20:27:27Z
dc.date.available2025-04-10T20:27:27Z
dc.date.issued2023
dc.description.abstractThe 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 leads to biased estimation and degraded inferences. This problem arises in detection and attribution analyses of changes in climate extremes because the covariates are estimated with uncertainty. It has not been studied even for the case of independent EIVs, let alone the case of dependent EIVs, due to the complex structure of GEV. Here we propose a general Monte Carlo corrected score method and extend it to address temporally correlated EIVs in GEV modeling with application to the detection and attribution analyses for climate extremes. Through extensive simulation studies, the proposed method provides an unbiased estimator and valid inference. In the application to the detection and attribution analyses of temperature extremes in central regions of China, with the proposed method, the combined anthropogenic and natural signal is detected in the change in the annual minimum of daily maximum and the annual minimum of daily minimum.
dc.description.reviewstatusReviewed
dc.description.scholarlevelFaculty
dc.identifier.citationLau, Y. T. A., Wang, T., Yan, J., & Zhang, X. (2023). Extreme value modeling with errors-in-variables in detection and attribution of changes in climate extremes. Statistics and Computing, 33(6), 125. https://doi.org/10.1007/s11222-023-10290-8
dc.identifier.urihttps://doi.org/10.1007/s11222-023-10290-8
dc.identifier.urihttps://hdl.handle.net/1828/21802
dc.language.isoen
dc.publisherStatistics and Computing
dc.rightsCC BY 4.0
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectcorrected score
dc.subjectestimating equation
dc.subjectmeasurement error
dc.subjectMonte Carlo
dc.subjectUN SDG 13: Climate Action
dc.subject#journal article
dc.subjectPacific Climate Impacts Consortium (PCIC)
dc.titleExtreme value modeling with errors-in-variables in detection and attribution of changes in climate extremes
dc.typeArticle

Files

Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
lau_yuenta_statcomput_2023.pdf
Size:
1.03 MB
Format:
Adobe Portable Document Format
Download

Collections

PCIC Publications
 
University of Victoria Libraries

We acknowledge and respect the Lək̓ʷəŋən (Songhees and Esquimalt) Peoples on whose territory the university stands, and the Lək̓ʷəŋən and W̱SÁNEĆ Peoples whose historical relationships with the land continue to this day.