Zobrazeno 1 - 10
of 24
pro vyhledávání: '"Béranger, Boris"'
Symbolic data analysis (SDA) aggregates large individual-level datasets into a small number of distributional summaries, such as random rectangles or random histograms. Inference is carried out using these summaries in place of the original dataset,
Externí odkaz:
http://arxiv.org/abs/2408.04419
Max-stable processes serve as the fundamental distributional family in extreme value theory. However, likelihood-based inference methods for max-stable processes still heavily rely on composite likelihoods, rendering them intractable in high dimensio
Externí odkaz:
http://arxiv.org/abs/2407.13958
The substantial growth of network traffic speed and volume presents practical challenges to network data analysis. Packet thinning and flow aggregation protocols such as NetFlow reduce the size of datasets by providing structured data summaries, but
Externí odkaz:
http://arxiv.org/abs/2008.13424
Logistic regression models are a popular and effective method to predict the probability of categorical response data. However inference for these models can become computationally prohibitive for large datasets. Here we adapt ideas from symbolic dat
Externí odkaz:
http://arxiv.org/abs/1912.03805
Symbolic data analysis has been proposed as a technique for summarising large and complex datasets into a much smaller and tractable number of distributions -- such as random rectangles or histograms -- each describing a portion of the larger dataset
Externí odkaz:
http://arxiv.org/abs/1908.11548
Estimation of extreme quantile regions, spaces in which future extreme events can occur with a given low probability, even beyond the range of the observed data, is an important task in the analysis of extremes. Existing methods to estimate such regi
Externí odkaz:
http://arxiv.org/abs/1904.08251
Autor:
Béranger, Boris
La prédiction de futurs évènements extrêmes est d’un grand intérêt dans de nombreux domaines tels que l’environnement ou la gestion des risques. Alors que la théorie des valeurs extrêmes univariées est bien connue, la complexité s’acc
Externí odkaz:
http://www.theses.fr/2016PA066004/document
The skew-normal and related families are flexible and asymmetric parametric models suitable for modelling a diverse range of systems. We show that the multivariate maximum of a high-dimensional extended skew-normal random sample has asymptotically in
Externí odkaz:
http://arxiv.org/abs/1810.00680
Symbolic data analysis (SDA) is an emerging area of statistics concerned with understanding and modelling data that takes distributional form (i.e. symbols), such as random lists, intervals and histograms. It was developed under the premise that the
Externí odkaz:
http://arxiv.org/abs/1809.03659
We consider the extremal properties of the highly flexible univariate extended skew-normal distribution. We derive the well-known Mills' inequalities and Mills' ratio for the extended skew-normal distribution and establish the asymptotic extreme-valu
Externí odkaz:
http://arxiv.org/abs/1805.03316