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UID:20260601T025823EDT-9439BFHv7h@132.216.98.100
DTSTAMP:20260601T065823Z
DESCRIPTION:Title: Can we identify a max-linear model on a directed acyclic
  graph by the tail correlation matrix?\n\nWe investigate multivariate regu
 larly varying random vectors with discrete spectral measure induced by a d
 irected acyclic graph (DAG). The tail dependence coefficient measures extr
 eme dependence between two vector components\, and we investigate how the 
 matrix of tail dependence coefficients can be used to identify the full de
 pendence structure of the random vector on a DAG or even the DAG itself. F
 urthermore\, we estimate the distributional model by the matrix of empiric
 al tail dependence coefficients. From these observations we want to infer 
 the causal dependence structure in the data. This is joint work with Nadin
 e Gissibl and Moritz Otto.\n	\n	[1] Gissibl\, N. and Klüppelberg\, C. (2015)
 Max-linear models on directed acyclic graphs.Under revision.[2] Gissibl\, 
 N.\, Klüppelberg\, C. and Otto\, M. (2017)Tail dependence of recursive max
 -linear models with regularly varying noise variables.Submitted.\n\n\n	 \n
 \n	L\n\n	 \n\n\n \n
DTSTART:20170907T193000Z
DTEND:20170907T203000Z
LOCATION:1205\, Burnside Hall\, CA\, QC\, Montreal\, H3A 0B9\, 805 rue Sher
 brooke Ouest
SUMMARY:Claudia Klüppelberg\, Technische Universität München
URL:/mathstat/channels/event/claudia-kluppelberg-techn
 ische-universitat-munchen-269970
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