23 citations · 48 across the 9 of their papers we have counts for
13 papers
Principal component analysis for max-stable distributions
Felix Reinbott, Anja Janßen
Principal component analysis (PCA) is one of the most popular dimension reduction techniques in statistics and is especially powerful when a multivariate distribution is concentrat…
Multivariate Regular Variation of Preferential Attachment Models
Anja Janßen, Max Ziegenbalg
We use the framework of multivariate regular variation to analyse the extremal behaviour of preferential attachment models. To this end, we follow a directed linear preferential at…
Invariance properties of limiting point processes and applications to clusters of extremes
Anja Janßen, Johan Segers
Motivated by examples from extreme value theory we introduce the general notion of a cluster process as a limiting point process of returns of a certain event in a time series. We…
Cluster based inference for extremes of time series
Holger Drees, Anja Janßen, Sebastian Neblung
We introduce a new type of estimator for the spectral tail process of a regularly varying time series. The approach is based on a characterizing invariance property of the spectral…
-means clustering of extremes
Anja Janßen, Phyllis Wan
The -means clustering algorithm and its variant, the spherical -means clustering, are among the most important and popular methods in unsupervised learning and pattern detect…
On a minimum distance procedure for threshold selection in tail analysis
Holger Drees, Anja Janßen, Sidney I. Resnick +1
Power-law distributions have been widely observed in different areas of scientific research. Practical estimation issues include how to select a threshold above which observations…
Spectral tail processes and max-stable approximations of multivariate regularly varying time series
Anja Janßen
A regularly varying time series as introduced in Basrak and Segers (2009) is a (multivariate) time series such that all finite dimensional distributions are multivariate regularly…
Conditional Extreme Value Models: Fallacies and Pitfalls
Holger Drees, Anja Janßen
Conditional extreme value models have been introduced by Heffernan and Resnick (2007) to describe the asymptotic behavior of a random vector as one specific component becomes extre…
The eigenvalues of the sample covariance matrix of a multivariate heavy-tailed stochastic volatility model
Anja Janßen, Thomas Mikosch, Mohsen Rezapour +1
We consider a multivariate heavy-tailed stochastic volatility model and analyze the large-sample behavior of its sample covariance matrix. We study the limiting behavior of its ent…
Joint exceedances of random products
Anja Janßen, Holger Drees
We analyze the joint extremal behavior of random products of the form for non-negative, independent regularly varying random vari…
A stochastic volatility model with flexible extremal dependence structure
Anja Janssen, Holger Drees
Stochastic volatility processes with heavy-tailed innovations are a well-known model for financial time series. In these models, the extremes of the log returns are mainly driven b…
Markov tail chains
Anja Janßen, Johan Segers
The extremes of a univariate Markov chain with regulary varying stationary marginal distribution and asymptotically linear behavior are known to exhibit a multiplicative random wal…
Joint Extremal Behavior of Hidden and Observable Time Series with an Application to GARCH Processes
Andree Ehlert, Ulf-Rainer Fiebig, Anja Janßen +1
We study the behavior of a real-valued and unobservable process (Y_t) under an extreme event of a related process (X_t) that is observable. Our analysis is motivated by the well-kn…