Autocorrelation test under frequent mean shifts
September 2026
Testing for the presence of autocorrelation is a fundamental problem in time series analysis. Classical methods such as the Box–Pierce test rely on the assumption of stationarity, necessitating the...
Sublinearly structured deep neural networks achieve feature learning consistency for compositional functions
Available online 2 July 2026
Over the past decade, deep neural networks (DNNs) have achieved remarkable success on complex machine learning tasks, yet the theoretical foundations of their performance remain incomplete. From a statistical...
Statistics in the Next Quarter-Century: Playing also in the Frontyard?
Available online 24 June 2026
Statistics has undergone remarkable development over the past decades while interacting increasingly with Data Science, Machine Learning, and Artificial Intelligence (AI). This perspective discusses...
Network perturbation aggregation for graphon estimation
Available online 13 June 2026
In recent years, various methods have been proposed to estimate the edge probability under the graphon model given a single observed network. Since the presence or absence of edges in the observed network...
Distributed inference for high-dimensional convoluted rank regression
Available online 9 June 2026
Convoluted rank regression is recently developed as a powerful tool to deal with outliers and heavy-tailed noise data. However, there is still a lack of suitable methods for convoluted rank regression...
Varying coefficient tensor regression
Available online 3 June 2026
We propose a new varying coefficient model for tensor data regression analysis. To manage the complexity of multi-dimensional tensors, we first employ a tensor partitioning strategy to reduce dimensionality,...
Robust spectral watermark for synthetic tabular data
Available online 28 April 2026
The rise of generative AI has enabled the production of high-fidelity synthetic tabular data across fields such as healthcare, finance, and public policy, raising growing concerns about data provenance...