Publications associées à Multivariate extremes over a random number of observations

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Sublinear Bounds on the Distinguishing Advantage for Multiple Samples

Serge Vaudenay

The maximal achievable advantage of a (computationally unbounded) distinguisher to determine whether a source

Z

is distributed according to distribution

P_0

P_1

, when given access to one sample of

Z

, is characterized by the statistical distance ...

Springer, Cham2020

Multivariate extremes over a random number of observations

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Chattez avec Graph Search

Functional peaks-over-threshold analysis

Unbiased Monte Carlo cluster updates with autoregressive neural networks

Sub‐asymptotic motivation for new conditional multivariate extreme models

Efficient ensemble summaries are inversely related to visual crowding

The Importance of Modeling Data Missingness in Algorithmic Fairness: A Causal Perspective

Inference on the Angular Distribution of Extremes

Max-infinitely divisible models and inference for spatial extremes

Fractional extreme distributions

Sublinear Bounds on the Distinguishing Advantage for Multiple Samples

Statistical Aspects of Wasserstein Distances

Sub‐asymptotic motivation for new conditional multivariate extreme models

Unbiased Monte Carlo cluster updates with autoregressive neural networks

Functional peaks-over-threshold analysis

Efficient ensemble summaries are inversely related to visual crowding

The Importance of Modeling Data Missingness in Algorithmic Fairness: A Causal Perspective

Inference on the Angular Distribution of Extremes

Sublinear Bounds on the Distinguishing Advantage for Multiple Samples

Fractional extreme distributions

Statistical Aspects of Wasserstein Distances

Max-infinitely divisible models and inference for spatial extremes