[Statlist] NEW SPEAKER / Research Seminar in Statistics *FRIDAY, 23 OCTOBER 2020* GSEM, University of Geneva

gsem-support-instituts g@em-@upport-|n@t|tut@ @end|ng |rom un|ge@ch
Thu Oct 22 12:13:29 CEST 2020


Dear All,

Due to unforeseen circumstances, Prof. Sofia Charlotta OLHEDE (EPFL) cannot give her presentation this Friday.

However, another speaker will present his study. So, it is with great pleasure that we invite you to our next Research Seminar.

Looking forward to seeing you


Organizers :                                                                                   
E. Cantoni - S. Engelke - D. La Vecchia - E. Ronchetti
S. Sperlich - F. Trojani - M.-P. Victoria-Feser


FRIDAY, 23 OCTOBER 2020 at 11:15am
ONLINE
Please join the Zoom research webinar: https://unige.zoom.us/j/99238951053?pwd=dkd5UlRlYXkvNzZicnY0UlBCeW5rdz09
Password: 419459


Semi-Supervised Inference in Extreme Value Analyzis
(joint with Hanan Ahmed and John Einmahl both from Tilburg University)
Chen ZHOU (https://personal.eur.nl/zhou/) - Erasmus University Rotterdam, Netherlands

ABSTRACT:
The aim of this study is to conduct extreme value analysis on a small amount of labeled data, with the help of a large amount of unlabeled data. The labeled sample consists of one response variable and multiple covariates. Assume that they are asymptotically dependent in the tail. In addition, there are unlabeled data consisting of the covariates only. Our goal is to estimate the extreme value index as well as high quantiles for the response variable. With the help of the unlabeled covariates, we can construct an estimator for the extreme value index of the response variable possessing asymptotic normality where the asymptotic variance is lower than the maximum likelihood estimator when using the labeled data only. The variance reduction is inherited in high quantile estimators. The performance of the asymptotic theories under a finite sample setup is demonstrated by a simulation study.


Visit the website: https://www.unige.ch/gsem/en/research/seminars/rcs/

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