18 citations · 93 across the 40 of their papers we have counts for
48 papers
Asymptotic theory for Bayesian inference and prediction: from the ordinary to a conditional Peaks-Over-Threshold method
Clément Dombry, Simone A. Padoan, Stefano Rizzelli
The Peaks Over Threshold (POT) method is the most popular statistical method for the analysis of univariate extremes. Even though there is a rich applied literature on Bayesian inf…
A new methodology to predict the oncotype scores based on clinico-pathological data with similar tumor profiles
Zeina Al Masry, Romain Pic, Clément Dombry +1
Introduction: The Oncotype DX (ODX) test is a commercially available molecular test for breast cancer assay that provides prognostic and predictive breast cancer recurrence informa…
Stone's theorem for distributional regression in Wasserstein distance
Clément Dombry, Thibault Modeste, Romain Pic
We extend the celebrated Stone's theorem to the framework of distributional regression. More precisely, we prove that weighted empirical distribution with local probability weights…
A large sample theory for infinitesimal gradient boosting
Clement Dombry, Jean-Jil Duchamps
Infinitesimal gradient boosting (Dombry and Duchamps, 2021) is defined as the vanishing-learning-rate limit of the popular tree-based gradient boosting algorithm from machine learn…
Distributional regression and its evaluation with the CRPS: Bounds and convergence of the minimax risk
Romain Pic, Clément Dombry, Philippe Naveau +1
The theoretical advances on the properties of scoring rules over the past decades have broadened the use of scoring rules in probabilistic forecasting. In meteorological forecastin…
Infinitesimal gradient boosting
Clément Dombry, Jean-Jil Duchamps
We define infinitesimal gradient boosting as a limit of the popular tree-based gradient boosting algorithm from machine learning. The limit is considered in the vanishing-learning-…