Extremal behaviour of aggregated data with an application to downscaling

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Ergodic Exploration Using Tensor Train: Applications in Insertion Tasks

In robotics, ergodic control extends the tracking principle by specifying a probability distribution over an area to cover instead of a trajectory to track. The original problem is formulated as a spectral multiscale coverage problem, typically requiring t ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2022

Multivariate extremes over a random number of observations

Simone Padoan, Stefano Rizzelli

The classical multivariate extreme-value theory concerns the modeling of extremes in a multivariate random sample, suggesting the use of max-stable distributions. In this work, the classical theory is extended to the case where aggregated data, such as max ...

WILEY2021

On the probability distribution of the local times of diagonally operator-self-similar Gaussian fields with stationary increments

Thomas Mountford, Kamran Kalbasi

In this paper, we study the local times of vector-valued Gaussian fields that are 'diagonally operator-selfsimilar' and whose increments are stationary. Denoting the local time of such a Gaussian field around the spatial origin and over the temporal unit h ...

INT STATISTICAL INST2020

Inference on the Angular Distribution of Extremes

Claudio Andri Semadeni

The spectral distribution plays a key role in the statistical modelling of multivariate extremes, as it defines the dependence structure of multivariate extreme-value distributions and characterizes the limiting distribution of the relative sizes of the co ...

EPFL2020

Distributionally Robust Optimization with Polynomial Densities: Theory, Models and Algorithms

Daniel Kuhn

In distributionally robust optimization the probability distribution of the uncertain problem parameters is itself uncertain, and a fictitious adversary, e.g., nature, chooses the worst distribution from within a known ambiguity set. A common shortcoming o ...

2020

The n-term Approximation of Periodic Generalized Levy Processes

Michaël Unser, Julien René Pierre Fageot, John Paul Ward

In this paper, we study the compressibility of random processes and fields, called generalized Levy processes, that are solutions of stochastic differential equations driven by d-dimensional periodic Levy white noises. Our results are based on the estimati ...

2020

Gaussian and sparse processes are limits of generalized Poisson processes

Michaël Unser, Julien René Pierre Fageot, Virginie Sophie Uhlmann

The theory of sparse stochastic processes offers a broad class of statistical models to study signals, far beyond the more classical class of Gaussian processes. In this framework, signals are represented as realizations of random processes that are soluti ...

2020

Fractional extreme distributions

Thomas Simon, Lotfi Boudabsa

We consider three classes of linear differential equations on distribution functions, with a fractional order alpha is an element of [0; 1]. The integer case alpha = 1 corresponds to the three classical extreme families. In general, we show that there is a ...

2020

Distributional Robustness in Mechanism Design

Cagil Kocyigit

Mechanism design theory examines the design of allocation mechanisms or incentive systems involving multiple rational but self-interested agents and plays a central role in many societally important problems in economics. In mechanism design problems, agen ...

EPFL2020

Simulation-based Anomaly Detection and Damage Localization: An application to Structural Health Monitoring

Jan Sickmann Hesthaven, Caterina Bigoni

We propose a simulation-based decision strategy for the proactive maintenance of complex structures with a particular application to structural health monitoring (SHM). The strategy is based on a data-driven approach which exploits an offline-online decomp ...

2020

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On the probability distribution of the local times of diagonally operator-self-similar Gaussian fields with stationary increments

Thomas Mountford, Kamran Kalbasi

INT STATISTICAL INST2020

Distributionally Robust Optimization with Polynomial Densities: Theory, Models and Algorithms

Daniel Kuhn

2020

Ergodic Exploration Using Tensor Train: Applications in Insertion Tasks

Sylvain Calinon, Suhan Narayana Shetty

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2022