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Concept

Elliptical distribution

Related publications (18)

Price Discrimination with Robust Beliefs

Thomas Alois Weber, Jun Han

This paper considers the problem of second-degree price discrimination when the type distribution is unknown or imperfectly specified by means of an ambiguity set. As robustness measure we use a performance index, equivalent to relative regret, which quant ...

Elsevier2023

Towards the Validation of Noise Experiments in the CROCUS Reactor Using the TRIPOLI-4 Monte Carlo Code in Analog Mode

Andreas Pautz, Vincent Pierre Lamirand, Oskari Ville Pakari

Intrinsic neutron noise experiments offer a non-invasive manner to measure the prompt decay constant or reactivity of fissile systems. Using the fluctuations in the density of fission chains, one can infer the kinetics parameters via correlation analysis s ...

2021

Further results on latent discourse models and word embeddings

Youssef Allouah

We discuss some properties of generative models for word embeddings. Namely, (Arora et al., 2016) proposed a latent discourse model implying the concentration of the partition function of the word vectors. This concentration phenomenon led to an asymptotic ...

Microtome Publishing2021

A Novel Approach for Classification and Forecasting of Time Series in Particle Accelerators

Fernando Perez Cruz

The beam interruptions (interlocks) of particle accelerators, despite being necessary safety measures, lead to abrupt operational changes and a substantial loss of beam time. A novel time series classification approach is applied to decrease beam time loss ...

2021

Distributionally Robust Inverse Covariance Estimation: The Wasserstein Shrinkage Estimator

Daniel Kuhn, Viet Anh Nguyen, Peyman Mohajerin Esfahani

We introduce a distributionally robust maximum likelihood estimation model with a Wasserstein ambiguity set to infer the inverse covariance matrix of a p-dimensional Gaussian random vector from n independent samples. The proposed model minimizes the worst ...

2022

Data-Aware Privacy-Preserving Machine Learning

Aleksei Triastcyn

In this thesis, we focus on the problem of achieving practical privacy guarantees in machine learning (ML), where the classic differential privacy (DP) fails to maintain a good trade-off between user privacy and data utility. Differential privacy guarantee ...

EPFL2020

Contributions to Likelihood-Based Modelling of Extreme Values

Léo Raymond-Belzile

xtreme value analysis is concerned with the modelling of extreme events such as floods and heatwaves, which can have large impacts. Statistical modelling can be useful to better assess risks even if, due to scarcity of measurements, there is inherently ver ...

EPFL2019

Statistical Applications of Random Matrix Theory: Comparison of Two Populations

Rémy Mariétan

During the last twenty years, Random matrix theory (RMT) has produced numerous results that allow a better understanding of large random matrices. These advances have enabled interesting applications in the domain of communication. Although this theory can ...

EPFL2019

Unsupervised Scalable Representation Learning for Multivariate Time Series

Martin Jaggi, Aymeric Daphnis Kévin Dieuleveut

Time series constitute a challenging data type for machine learning algorithms, due to their highly variable lengths and sparse labeling in practice. In this paper, we tackle this challenge by proposing an unsupervised method to learn universal embeddings ...

NEURAL INFORMATION PROCESSING SYSTEMS (NIPS)2019

Robust Portfolio Optimization

Zheng Wei Yap

Since the 2008 Global Financial Crisis, the financial market has become more unpredictable than ever before, and it seems set to remain so in the forseeable future. This means an investor faces unprecedented risks, hence the increasing need for robust port ...

EPFL2017

Contributions to Modelling Extremes of Spatial Data

Linda Frossard

The increasing interest in using statistical extreme value theory to analyse environmental data is mainly driven by the large impact extreme events can have. A difficulty with spatial data is that most existing inference methods for asymptotically justifie ...

EPFL2017

Elliptical tempered stable distribution

Elliptical distributions are useful for modelling multivariate data, multivariate normal and Student t distributions being two special classes. In this paper, we provide a definition for the elliptical tempered stable (ETS) distribution based on its charac ...

Routledge Journals, Taylor & Francis Ltd2016

Multi-Period Portfolio Optimization: Translation of Autocorrelation Risk to Excess Variance

Napat Rujeerapaiboon

Growth-optimal portfolios are guaranteed to accumulate higher wealth than any other investment strategy in the long run. However, they tend to be risky in the short term. For serially uncorrelated markets, similar portfolios with more robust guarantees hav ...

2016

Message-Passing Algorithms for Quadratic Minimization

Nicholas Ruozzi

Gaussian belief propagation (GaBP) is an iterative algorithm for computing the mean (and variances) of a multivariate Gaussian distribution, or equivalently, the minimum of a multivariate positive definite quadratic function. Sufficient conditions, such as ...

Microtome Publishing2013

Robust Multivariate and Nonlinear Time Series Models

Time series modeling and analysis is central to most financial and econometric data modeling. With increased globalization in trade, commerce and finance, national variables like gross domestic productivity (GDP) and unemployment rate, market variables lik ...

EPFL2010

Geostatistical simulation of 2D fields of raindrop size distributions at the meso-gamma scale

Alexis Berne, Marc Schleiss

The large variability of the drop size distribution (DSD) in space and time must be taken into account to improve remote sensing of precipitation. The ability to simulate a large number of 2D fields of DSD sharing the same statistical properties provides a ...

American Geophysical Union2009

Static Models of Derivative-Coordinates Phase Spaces for Multivariate Time Series Classification: An Application to Signature Verification

Jonas Richiardi, Andrzej Drygajlo

Multivariate time series are sequences, whose order is provided by a time index; thus, most classifiers used on such data treat time as a special quantity, and encode it, structurally in a model. A typical example of such models is the hidden Markov model, ...

Springer-Verlag New York, Ms Ingrid Cunningham, 175 Fifth Ave, New York, Ny 10010 Usa2009

Estimation of value of travel-time savings using Mixed Logit models

Michel Bierlaire

In this paper, we discuss some of the issues that arise with the computation of the implied value of travel-time savings in the case of discrete choice models allowing for random taste heterogeneity. We specifically look at the case of models producing a n ...

2005