Zobrazeno 1 - 10
of 428
pro vyhledávání: '"Klüppelberg, Claudia"'
Recursive max-linear vectors provide models for the causal dependence between large values of observed random variables as they are supported on directed acyclic graphs (DAGs). But the standard assumption that all nodes of such a DAG are observed is
Externí odkaz:
http://arxiv.org/abs/2306.15356
We propose a new method to estimate a root-directed spanning tree from extreme data. A prominent example is a river network, to be discovered from extreme flow measured at a set of stations. Our new algorithm utilizes qualitative aspects of a max-lin
Externí odkaz:
http://arxiv.org/abs/2102.06197
Autor:
Buck, Johannes, Klüppelberg, Claudia
Recursive max-linear vectors model causal dependence between node variables by a structural equation model, expressing each node variable as a max-linear function of its parental nodes in a directed acyclic graph (DAG) and some exogenous innovation.
Externí odkaz:
http://arxiv.org/abs/2003.00362
Publikováno v:
Annals of Applied Probability 32, 1-45, 2022
Motivated by extreme value theory, max-linear Bayesian networks have been recently introduced and studied as an alternative to linear structural equation models. However, for max-linear systems the classical independence results for Bayesian networks
Externí odkaz:
http://arxiv.org/abs/2002.09233
Autor:
Klüppelberg, Claudia, Krali, Mario
Recursive max-linear vectors model causal dependence between its components by expressing each node variable as a max-linear function of its parental nodes in a directed acyclic graph and some exogenous innovation. Motivated by extreme value theory,
Externí odkaz:
http://arxiv.org/abs/1912.03968
We estimate the parameter of a stationary time series process by minimizing the integrated weighted mean squared error between the empirical and simulated characteristic function, when the true characteristic functions cannot be explicitly computed.
Externí odkaz:
http://arxiv.org/abs/1904.08276
We provide a new extension of Breiman's Theorem on computing tail probabilities of a product of random variables to a multivariate setting. In particular, we give a complete characterization of regular variation on cones in $[0,\infty)^d$ under rando
Externí odkaz:
http://arxiv.org/abs/1904.06824
Autor:
Klüppelberg, Claudia, Pham, Viet Son
We estimate model parameters of L\'evy-driven causal CARMA random fields by fitting the empirical variogram to the theoretical counterpart using a weighted least squares (WLS) approach. Subsequent to deriving asymptotic results for the variogram esti
Externí odkaz:
http://arxiv.org/abs/1902.04962
We introduce a statistical model for operational losses based on heavy-tailed distributions and bipartite graphs, which captures the event type and business line structure of operational risk data. The model explicitly takes into account the Pareto t
Externí odkaz:
http://arxiv.org/abs/1902.03041
We study Bayesian networks based on max-linear structural equations as introduced in Gissibl and Kl\"uppelberg [16] and provide a summary of their independence properties. In particular we emphasize that distributions for such networks are generally
Externí odkaz:
http://arxiv.org/abs/1901.03948