1 citations · 1 across the 5 of their papers we have counts for
5 papers
Online Bootstrap Inference for the Trend of Nonstationary Time Series
Thomas Nagler, Tobias Brock, Nicolai Palm
This article proposes an online bootstrap scheme for nonparametric level estimation in nonstationary time series. Our approach applies to a broad class of level estimators expressi…
Uniform central limit theorems for non-stationary processes via relative weak convergence
Nicolai Palm, Thomas Nagler
Statistical inference for non-stationary data is hindered by the failure of classical central limit theorems (CLTs), not least because there is no fixed Gaussian limit to converge…
Label-wise Aleatoric and Epistemic Uncertainty Quantification
Yusuf Sale, Paul Hofman, Timo Löhr +3
We present a novel approach to uncertainty quantification in classification tasks based on label-wise decomposition of uncertainty measures. This label-wise perspective allows unce…
Second-Order Uncertainty Quantification: Variance-Based Measures
Yusuf Sale, Paul Hofman, Lisa Wimmer +2
Uncertainty quantification is a critical aspect of machine learning models, providing important insights into the reliability of predictions and aiding the decision-making process…
An Online Bootstrap for Time Series
Nicolai Palm, Thomas Nagler
Resampling methods such as the bootstrap have proven invaluable in the field of machine learning. However, the applicability of traditional bootstrap methods is limited when dealin…