6 papers
Empirical tail dependence functions in high dimensions: uniform linearizations and inference
Axel Bücher, Yeonjoon Choi, Katharina Effertz +1
The analysis of extremal dependence in high dimensions is a key challenge in modern extreme-value statistics. Existing methodology primarily focuses on modeling and estimation of e…
Extreme Value Analysis based on Blockwise Top-Two Order Statistics
Axel Bücher, Erik Haufs
Extreme value analysis for time series is often based on the block maxima method, in particular for environmental applications. In the classical univariate case, the latter is base…
Dimension Reduction in Multivariate Extremes via Latent Linear Factor Models
Alexis Boulin, Axel Bücher
We propose a new and interpretable class of high-dimensional tail dependence models based on latent linear factor structures. Specifically, extremal dependence of an observable vec…
Consistency of M-estimators for non-identically distributed data: the case of fixed-design distributional regression
Axel Bücher, Johan Segers, Torben Staud
This paper explores strong and weak consistency of M-estimators for non-identically distributed data, extending prior work. Emphasis is given to scenarios where data is viewed as a…
The empirical copula process in high dimensions: Stute's representation and applications
Axel Bücher, Cambyse Pakzad
The empirical copula process, a fundamental tool for copula inference, is studied in the high dimensional regime where the dimension is allowed to grow to infinity exponentially in…
On the lack of weak continuity of Chatterjee's correlation coefficient
Axel Bücher, Holger Dette
Chatterjee's correlation coefficient has recently been proposed as a new association measure for bivariate random vectors that satisfies a number of desirable properties. Among the…