5 papers
Benchmark of Likelihood-Free Inference Methods based on Neural and Optimal Transport Approaches
Samira Aka, Marie Kratz, Philippe Naveau
Simulation-based inference (SBI) has become an increasingly important framework for parameter estimation in models for which simulation is feasible, including cases where likelihoo…
Likelihood-Free Inference for Multivariate Generalized Pareto Models
Samira Aka, Marie Kratz, Philippe Naveau
Likelihood-based inference for multivariate extreme-value models is often unreliable or infeasible when likelihoods are intractable or supports are discrete. This challenge is part…
Extreme Geometric Quantiles Under Minimal Assumptions, with a Connection to Tukey Depth
Sibsankar Singha, Marie Kratz, Sreekar Vadlamani
Geometric (also known as spatial) quantiles, introduced by Chaudhury and representing one of the three principal approaches to defining multivariate quantiles, have been well studi…
Discriminating Tail Behavior Using Halfspace Depths: Population and Empirical Perspectives
Sibsankar Singha, Marie Kratz, Sreekar Vadlamani
We study the empirical version of halfspace depths with the objective of establishing a connection between the rates of convergence and the tail behaviour of the corresponding unde…
Comparing Multivariate Distributions: A Novel Approach Using Optimal Transport-based Plots
Sibsankar Singha, Marie Kratz, Sreekar Vadlamani
Quantile-Quantile (Q-Q) plots are widely used for assessing the distributional similarity between two datasets. Traditionally, Q-Q plots are constructed for univariate distribution…