24 citations · 48 across the 11 of their papers we have counts for
16 papers
Parsimonious Factor Models for Asymmetric Dependence in Multivariate Extremes
Pavel Krupskii, Boris Béranger
Modelling multivariate extreme events is essential when extrapolating beyond the range of observed data. Parametric models that are suitable for real-world extremes must be flexibl…
Modeling extremal dependence in multivariate and spatial problems: a practical perspective
Boris Beranger, Simone A. Padoan
From environmental sciences to finance, there is a growing demand for methods that can assess the risks of extreme events beyond those observed in available data. Extrapolating ext…
Analysing symbolic data by pseudo-marginal methods
Yu Yang, Matias Quiroz, Boris Beranger +2
Symbolic data analysis (SDA) aggregates large individual-level datasets into a small number of distributional summaries, such as random rectangles or random histograms. The inferen…
Fast and flexible inference for spatial extremes
Peng Zhong, Scott A. Sisson, Boris Beranger
Statistical modelling of spatial extreme events has gained increasing attention over the last few decades with max-stable processes, and more recently -Pareto processes, becomin…
Likelihood-based inference for modelling packet transit from thinned flow summaries
Prosha A. Rahman, Boris Beranger, Matthew Roughan +1
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 re…
Logistic regression models for aggregated data
Tom Whitaker, Boris Beranger, Scott A. Sisson
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…