Concentration bounds for statistical learning for time dependent data

Wei-Biao Wu

High-dimensional copula-based dependence

Irène Gijbels, Steven De Keyser

Vine copulas for stochastic volatility

Alexander John McNeil

Sparse M-estimators in semi-parametric copula models

Jean-David Fermanian, Benjamin Poignard

Change-Point Synchronization Testing in Multiple Time-Series

Soham Bonnerjee, Sayar Karmakar, Maggie Cheng, Wei Biao Wu

Multiple change point detection in functional data with applications to biomechanical fatigue data

Patrick Bastian, Rupsa Basu, Holger Dette

Bootstrap convergence rates for the maximum of an increasing number of autocovariances and autocorrelations under strict stationarity

Alexander Braumann, Marco Meyer, Jens-Peter Kreiss

Signpost testing to navigate the high-dimensional parameter space of the linear regression model

Wessel N. van Wieringen

High-dimensional vine copula regression for mixed continuous-ordinal features

Özge Şahin

Reviving pseudo-inverses: Asymptotic properties of large dimensional Moore-Penrose and Ridge-type inverses with applications

Taras Bodnar, Nestor Parolya

Isotonic Distributional Regression - Likelihood Ratio Order and Total Positivity

Lutz Duembgen, Alexandre Moesching, Philip Stange

Single-index mixture cure model under monotonicity constraints

Eni Musta, Tsz Pang Yuen

Convex loss selection via score matching

Oliver Feng, Yu-Chun Kao, Min Xu, Richard J. Samworth

A comparison between Dirichlet process-based inference and shape-constrained inference for the Wicksell's inverse problem.

Francesco Gili

Detection of breaks in weak location time series models with quasi-Fisher scores

Christian Francq, Lorenzo Trapani, Jean-Michel Zakoian

Online Detection of Changes in Moment-Based Projections: When to Retrain Deep Learners or Update Portfolios?

Ansgar Steland

Semi-continuous time series for sparse data with volatility clustering

Sarka Hudecova, Michal Pesta

Interactive Effects of Temperature and Precipitation on Global Economic Growth

Hande Karabiyik, Thomas Leirvik, Menghan Yuan

Recovering latent linkage structures and spillover effects with structural breaks in panel data models

Ryo Okui, Wendun Wang, Yutao Sun

Joint Modeling and Estimation of Global and Local Cross-Sectional Dependence in Panel Data Sets

Julia Schaumburg, Quint Wiersma, Siem Jan Koopman

Age-specific transmission dynamics of SARS-CoV-2 during the first two years of the pandemic

Otilia Boldea, Amir Alipoor, Sen Pei, Jeffrey Shaman, Ganna Rozhnova

Plausible GMM via Avenue Bayes

Victor Chernozhukov, Christian B. Hansen, Lingwei Kong, Weining Wang

The use of neural networks and PCA dimensionality reduction in filling missing fragments in high-dimensional time series

Ewa Skubalska-Rafajłowicz, Adam Krzyżak, Michał Piórek

Test bench automation with Safe Active Learning using Gaussian Processes

Christoph Zimmer

Multivariate Probabilistic CRPS Learning with an Application to Day-Ahead Electricity Price Forecasting

Jonathan Berrisch, Florian Ziel

Semi-Structured Regression

David Rügamer

Forecasting the electricity demand flexibility via data-driven inverse optimization

ADRIAN Esteban Perez, YASHAR Ghiassi-Farrokhfal, DEREK Bunn

Spectral calibration of time-inhomogeneous exponential Lévy models

Loek Koorevaar, Jakob Söhl, Stan Tendijck

Kolmogorov-Smirnov Distribution and Self-Similarity of fractional Brownian motion

Sergio Bianchi, Daniele Angelini, Massimiliano Frezza, Augusto Pianese

Asymptotic Efficiency for Fractional Brownian Motion

Grégoire Szymanski

Bridge simulation for manifold-valued semimartingales

Marc Corstanje, Frank van der Meulen, Moritz Schauer, Stefan Sommer

Constructing Confidence Intervals for Compound Poisson Process

Qinhao Wu, Marek Skarupski

Multivariate functional additive mixed models

Alexander Volkmann, Almond Stöcker, Fabian Scheipl, Sonja Greven

Multivariate Functional Spatial Data: A Principal Component Analysis Approach

Sophie Dabo

Spatial Dependencies in Stock Returns

Dominik Wied

New STINARMA class of models in the analysis of space-time series of counts

Ana Martins, Manuel G. Scotto, Christian Weiß, Sónia Gouveia

A non-stationary spatio-temporal precipitation model for Austria

Corinna Perchtold, Johan Lindström, Evelyn Buckwar

Tests of exogeneity in proportional hazards models with censored data

Gilles Crommen, Jean-Pierre Florens, Ingrid Van Keilegom

A general framework for compound-Poisson INAR and INGARCH models

Barbora Sobolová, Johannes Bracher

Predictive inference for count data time series

Maxime Faymonville, Efstathios Paparoditis, Carsten Jentsch

Joint vector-autoregressive modeling of real- and integer-valued time series with full autoregressive parameter range

Carsten Jentsch, Maxime Faymonville, Miguel Kankeu Tedjouka

Absolute regularity of non-stationary count time series

Anne Leucht, Michael H. Neumann, Paul Doukhan

Covariate-adjusted Sensor Outputs for Structural Health Monitoring: A Functional Data Approach

Philipp Wittenberg

A Technical Note on Self-starting Regression Control Charts

Alessandro Di Bucchianico

Predictive Ratio Cusum (PRC): A Bayesian Approach in Online Change Point Detection of Short Runs

Konstantinos Bourazas, Frederic Sobas, Panagiotis Tsiamyrtzis

AI and the Future of Work in Analytics: insights from a first attempt to Augment ChatGPT and to assess the Quality of Generative AI Analytics capabilities

Inez Zwetsloot

Modern Extremes: Methods, Theories, and Algorithms

Johannes Lederer

Image classification: A new statistical viewpoint

Sophie Langer

Dropout Regularization Versus \ell_2-Penalization in the Linear Model

Gabriel Clara, Sophie Langer, Johannes Schmidt-Hieber

Inference on derivatives of high dimensional regression function with deep neural network(NN)

Weining Wang

Likelihood-based methods for low frequency diffusion data

Sven Wang, Matteo Giordano

Statistical guarantees for stochastic Metropolis-Hastings

Sebastian Bieringer, Gregor Kasieczka, Maximilian F. Steffen, Mathias Trabs

The Bernstein-von Mises theorem for semiparametric mixtures

Stefan Franssen, Jeanne Nguyen, Aad van der Vaart

Weak dependence and optimal quantitative self-normalized central limit theorems

Moritz Jirak

Recent advances in large sample correlation matrices and their applications

Johannes Heiny

Linear shrinkage for optimization in high dimensions

Theresia van Essen, Naqi Huang, Nestor Parolya

A test on the location of tangency portfolio for small sample size and singular covariance matrix

Svitlana Drin, Stepan Mazur, Stanislas Muhinyuza

Joint empirical risk minimization for instance-dependent positive-unlabeled data

Jan Mielniczuk, Wojciech Rejchel, Paweł Teisseyre

Analysis of the rate of convergence of an over-parametrized convolutional neural network image classifier learned by gradient descent

Michael Kohler, Adam Krzyzak

Bayes Risk Consistency of Nonparametric Classification Rules for Spike Trains Data

Miroslaw Pawlak

Advances in Adversarial Risk Analysis

Fabrizio Ruggeri

Modeling multivariate ordinal time series

Malte Jahn

Using Spatial Ordinal Patterns for Non-parametric Testing of Spatial Dependence

Christian Weiß, Hee-Young Kim

Multivariate Motion Patterns and Applications to Rainfall Radar Data

Alexander Schnurr, Svenja Fischer, Marco Oesting

Depth patterns

Annika Betken, Alexander Schnurr

Hierarchical variable clustering based on the predictive strength between random vectors

Sebastian Fuchs, Yuping Wang

Kendall’s tau estimator for zero-inflated count data

Elisa Perrone, Edwin van den Heuvel, Zhuozhao Zhan

Fast estimation of Kendall's Tau and conditional Kendall's Tau matrices under structural assumptions

Rutger van der Spek, Alexis Derumigny

Inference via robust optimal transportation: theory and methods

Davide La Vecchia, Hang Liu, Matthieu Lerasle, Yiming Ma

Inference for topological data analysis

Wolfgang Polonik, Johannes Krebs, Benjamin Roycraft

Multi-study learning approaches

Roberta De Vito

Testing for sufficient follow-up in survival data with immunes

Tsz Pang Yuen, Eni Musta

Combining profile likelihood with Bayesian estimation for Crow-AMSAA process

Marek Skarupski

Optimizing the allocation of trials to sub-regions in multi-environment crop variety testing for the case of correlated genotype effects

Maryna Prus

Multivariate dependence based on diagonal sections with an application to welfare analysis

ANA PEREZ, MERCEDES PRIETO-ALAIZ, KOEN DECANCQ

Revisiting the Williamson transform in the context of multivariate Archimedean copulas

Nicolas Dietrich, Wolfgang Trutschnig, Thimo Kasper

Approximation of copulas using Cramér-von Mises statistic: regularization and model selection

Eckhard Liebscher

A Wasserstein perspective of Vanilla GANs

Lea Kunkel, Mathias Trabs

Asymptotic Theory for Constant Step Size Stochastic Gradient Descent

Jiaqi Li, Zhipeng Lou, Stefan Richter, Wei Biao Wu

A Continuous-time Stochastic Gradient Descent Method for Continuous Data

Kexin Jin, Jonas Latz, Chenguang Liu, Carola-Bibiane Schönlieb

A Platform-Agnostic Deep Reinforcement Learning Framework for Effective Sim2Real Transfer in Autonomous Driving

Li Dianzhao, Ostap Okhrin

Adaptive factor modeling

Matthias Fengler

Fitting bivariate copula mixture models

Philipp Haid, Aleksey Min, Thomas Nagler, Yarema Okhrin

Instabilities in Time Dependent Functional Profiles: Theory and Computation

Matus Maciak, Sebastiano Vitali

Smoothing for a SIR process

Frank van der Meulen, Daniel Brus, Moritz Schauer

Nonparametric estimation of the interaction function in particle system models

Denis Belomestny, Mark Podolskij, Shi-Yuan Zhou

Statistical analysis of a stochastic boundary model for high-frequency data from a limit order book

Markus Bibinger

Nonparametric Bayesian volatility estimation for gamma-driven stochastic differential equations

Shota Gugushvili

Statistical inference for intrinsic wavelet estimators of covariance matrices in a log-Euclidean manifold

Rainer von Sachs, Daniel Rademacher, Johannes Krebs

Time-Varying Covariance Matrices Estimation by Nonlinear Wavelet Thresholding in a Log-Euclidean Riemannian Manifold

Gabriel Bailly, Rainer von Sachs

Using High-Frequency Data to Improve Forecast Evaluation

Hajo Holzmann, Bernhard Klar

On the weak convergence of the function-indexed sequential empirical process for nonstationary time series

Florian Alexander Scholze

Towards a statistical foundation for machine learning methods

Johannes Schmidt-Hieber