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UID:20260604T045732EDT-3576OF1cCv@132.216.98.100
DTSTAMP:20260604T085732Z
DESCRIPTION:Quantile LASSO in Nonparametric Models with Changepoints Under 
 Optional Shape Constraints\n\n\n	Abstract:\n\n\nNonparametric models are po
 pular modeling tools because of their natural overall flexibility. In our 
 approach\, we apply nonparametric techniques for panel data structures wit
 h changepoints and optional shape constraints and the estimation is perfor
 med in a fully data driven manner by utilizing atomic pursuit methods – LA
 SSO regularization techniques in particular. However\, in order to obtain 
 robust estimates and\, also\, to have a more complex insight into the unde
 rlying data structure\, we target conditional quantiles rather then the co
 nditional mean only. The whole estimation process and the following infere
 nce become both more challenging but the results are more useful in practi
 cal applications. The underlying model is firstly introduced and some theo
 retical results are presented. The proposed methodology is applied for a r
 eal data scenario and some finite sample properties are investigated via a
 n extensive simulation study. This is a joint work with Ivan Mizera\, Univ
 ersity of Alberta and Gabriela Ciuperca\, University of Lyon\n
DTSTART:20180914T193000Z
DTEND:20180914T203000Z
LOCATION:Room 1104\, Burnside Hall\, CA\, QC\, Montreal\, H3A 0B9\, 805 rue
  Sherbrooke Ouest
SUMMARY:Matus Maciak (Charles University)
URL:https://www.mcgill.ca/mathstat/channels/event/matus-maciak-charles-univ
 ersity-289626
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