:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Authors:	Betken, Annika, Micali, Giorgio, Schmidt-Hieber, Johannes
Format:	Preprint
Published:	2025
Subjects:	Statistics Theory
Online Access:	https://arxiv.org/abs/2502.03099
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Symmetry Testing in Time Series using Ordinal Patterns: A U-Statistic Approach
by: Betken, Annika, et al.
Published: (2026)

Central limit theorems for the outputs of fully convolutional neural networks with time series input
by: Betken, Annika, et al.
Published: (2026)

Ordinal Patterns Based Testing of Spatial Independence in Irregular Spatial Structures
by: Micali, Giorgio, et al.
Published: (2026)

Depth Patterns and their Applications in Animal Tracking
by: Betken, Annika, et al.
Published: (2024)

Higher-order approximation for uncertainty quantification in time series analysis
by: Betken, Annika, et al.
Published: (2022)

Extending Characterizations of Multivariate Laws via Distance Distributions
by: Betken, Annika, et al.
Published: (2025)

Local convergence rates of the nonparametric least squares estimator with applications to transfer learning
by: Schmidt-Hieber, Johannes, et al.
Published: (2022)

Test for independence of long-range dependent time series using distance covariance
by: Betken, Annika, et al.
Published: (2021)

Spike-timing-dependent Hebbian learning as noisy gradient descent
by: Dexheimer, Niklas, et al.
Published: (2025)

Training Diagonal Linear Networks with Stochastic Sharpness-Aware Minimization
by: Clara, Gabriel, et al.
Published: (2025)

On the VC dimension of deep group convolutional neural networks
by: Sepliarskaia, Anna, et al.
Published: (2024)

Dropout Regularization Versus $\ell_2$-Penalization in the Linear Model
by: Clara, Gabriel, et al.
Published: (2023)

Differentially Private Algorithms for Linear Queries via Stochastic Convex Optimization
by: Micali, Giorgio, et al.
Published: (2024)

A supervised deep learning method for nonparametric density estimation
by: Bos, Thijs, et al.
Published: (2023)

Codivergences and information matrices
by: Derumigny, Alexis, et al.
Published: (2023)

Generative Modelling via Quantile Regression
by: Schmidt-Hieber, Johannes, et al.
Published: (2024)

Hyper Input Convex Neural Networks for Shape Constrained Learning and Optimal Transport
by: Hundrieser, Shayan, et al.
Published: (2026)

A Bootstrap Test for Independence of Time Series Based on the Distance Covariance
by: Betken, Annika, et al.
Published: (2021)

Lower bounds for the trade-off between bias and mean absolute deviation
by: Derumigny, Alexis, et al.
Published: (2023)

Improving the Convergence Rates of Forward Gradient Descent with Repeated Sampling
by: Dexheimer, Niklas, et al.
Published: (2024)

Convergence guarantees for forward gradient descent in the linear regression model
by: Bos, Thijs, et al.
Published: (2023)

A novel statistical approach to analyze image classification
by: Chen, Juntong, et al.
Published: (2022)

Understanding the Effect of GCN Convolutions in Regression Tasks
by: Chen, Juntong, et al.
Published: (2024)

Asymptotics of Stochastic Gradient Descent with Dropout Regularization in Linear Models
by: Li, Jiaqi, et al.
Published: (2024)

On High-Dimensional Change-Point Detection Based on Pairwise Distances
by: Ghoshal, Spandan, et al.
Published: (2025)

Change-Point Detection in Dynamic Networks with Missing Links
by: Enikeeva, Farida, et al.
Published: (2021)

Change Point Detection and Mean-Field Dynamics of Variable Productivity Hawkes Processes
by: Kresin, Conor, et al.
Published: (2025)

Change-Point Detection for Object-valued Time Series
by: Zhang, Yi, et al.
Published: (2026)

Detecting Abrupt Changes in Point Processes: Fundamental Limits and Applications
by: Brandenberger, Anna, et al.
Published: (2025)

Sequential Eigenvalue Statistics for Change-Point Detection in Covariance Matrices
by: Dörnemann, Nina, et al.
Published: (2024)

Functional Sieve Bootstrap for the Partial Sum Process with Application to Change-Point Detection
by: Paparoditis, Efstathios, et al.
Published: (2024)

Change Point Detection in Pairwise Comparison Data with Covariates
by: Han, Yi, et al.
Published: (2024)

Online Kernel CUSUM for Change-Point Detection
by: Wei, Song, et al.
Published: (2022)

Noise-contrastive Online Change Point Detection
by: Puchkin, Nikita, et al.
Published: (2022)

Adaptive Matrix Change Point Detection: Leveraging Structured Mean Shifts
by: Zhang, Xinyu, et al.
Published: (2024)

Detection and Mode-Identification of Multiple Change Points in Tensor Factor Models
by: Zhang, Yuqi, et al.
Published: (2026)

Single Change-Point Detection via Energy Distance with Application to Genomic Data
by: Ratnasingam, Suthakaran
Published: (2026)

Theoretical Foundations of Ordinal Multidimensional Scaling, Including Internal and External Unfolding
by: Arias-Castro, Ery, et al.
Published: (2023)

Inference on Dynamic Spatial Autoregressive Models with Change Point Detection
by: Cen, Zetai, et al.
Published: (2024)

Optimal Change Point Detection and Inference in the Spectral Density of General Time Series Models
by: Mosaferi, Sepideh, et al.
Published: (2025)