B. Beranger

Latest

• Logistic Regression Models for Aggregated Data
• Using symbolic data to understand underlying data behaviour
• High-dimensional inference using the extremal skew-t process
• Estimation and uncertainty quantification for extreme quantile regions
• Estimation and uncertainty quantification for extreme quantile regions
• Fitting models to underlying data using aggregates
• Logistic regression models for aggregated data
• High-dimensional inference for max-stable processes
• Estimation and uncertainty quantification for extreme quantile regions
• High-dimensional inference for max-stable processes
• Likelihood-based inference for modelling packet transit from thinned flow summaries
• Composite likelihood and logistic regression models for aggregated data
• Composite likelihood methods for histogram-valued random variables
• Estimating Equations for Data Summaries
• Constructing likelihood functions for interval-valued random variables
• Advances in the analysis of aggregated data
• New models for symbolic data analysis
• New Models for Symbolic Data
• Extremal properties of the multivariate extended skew-normal distribution, Part B
• Extremal properties of the univariate extended skew-normal distribution, Part A
• Tail density estimation for exploratory data analysis using kernel methods
• Inference for extremal-$t$ and skew-$t$ max-stable models in high dimensions
• First steps in the analysis of Symbolic Data
• Models for Extremal Dependence Derived from Skew-symmetric Families
• A composite likelihood based approach for max-stable processes using histogram-valued variables
• On some features of the skewed families of max-stable processes
• Extreme Dependence Models