Zobrazeno 1 - 10
of 118
pro vyhledávání: '"Dombry, Clement"'
This paper investigates predictive probability inference for classification tasks using random forests in the context of imbalanced data. In this setting, we analyze the asymptotic properties of simplified versions of the original Breiman's algorithm
Externí odkaz:
http://arxiv.org/abs/2408.01777
We review some recent development in the theory of spatial extremes related to Pareto Processes and modeling of threshold exceedances. We provide theoretical background, methodology for modeling, simulation and inference as well as an illustration to
Externí odkaz:
http://arxiv.org/abs/2407.05699
Accurate precipitation forecasts have a high socio-economic value due to their role in decision-making in various fields such as transport networks and farming. We propose a global statistical postprocessing method for grid-based precipitation ensemb
Externí odkaz:
http://arxiv.org/abs/2407.02125
Proper scoring rules are an essential tool to assess the predictive performance of probabilistic forecasts. However, propriety alone does not ensure an informative characterization of predictive performance and it is recommended to compare forecasts
Externí odkaz:
http://arxiv.org/abs/2407.00650
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 inference for the POT method there is no asymptotic theory for such propo
Externí odkaz:
http://arxiv.org/abs/2310.06720
Introduction: The Oncotype DX (ODX) test is a commercially available molecular test for breast cancer assay that provides prognostic and predictive breast cancer recurrence information for hormone positive, HER2-negative patients. The aim of this stu
Externí odkaz:
http://arxiv.org/abs/2303.06966
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 satisfying the conditions of Stone's theorem provide universally cons
Externí odkaz:
http://arxiv.org/abs/2302.00975
Autor:
Dombry, Clement, Duchamps, Jean-Jil
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 learning. It is characterized as the solution of a nonlinear ordinary diffe
Externí odkaz:
http://arxiv.org/abs/2210.00736
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 forecasting, statistical postprocessing techniques are essential to improve the
Externí odkaz:
http://arxiv.org/abs/2205.04360
Autor:
Dombry, Clément, Duchamps, Jean-Jil
Publikováno v:
In Stochastic Processes and their Applications April 2024 170
