The Institute of Statistical Mathematics

Power With Confounders in Clinical Trials Using External Controls

ABSTRACT When the number of patients is limited in clinical trials for rare diseases and pediatric developments, enrolling sufficient participants for confirmatory randomized clinical trials is difficult. Alternative trial designs using external controls have been proposed: augme...

Causality-integrated graph learning for multi-endpoint toxicity prediction

Toxicity prediction remains a longstanding challenge in the safety assessment of numerous artificial chemicals. While conventional single-endpoint models are effective for predicting many molecular properties, they fail to capture the complex mechanisms underlying toxicity, which...

Lipschitz continuously smoothing accelerated proximal gradient method

This paper presents smoothing proximal gradient methods using smooth approximation that is Lipschitz continuous with respect to a smoothing parameter in a non-differentiable term in an objective function. Furthermore, an accelerated proximal gradient method exploiting the strong...

Semantic Schema and Group Differences in Everyday Conceptions of Happiness and Unhappiness: An Investigation with a Word-association Network

What does happiness mean to ordinary people? Answering this requires examining not only the components people associate with happiness, but also how these components are organized and structurally connected into a semantic schema. The same questions apply to its conceptual counte...

Diffusion Geometry for Galaxy Bulk-Flow Reconstruction II: Three-Dimensional Potential-Flow Reconstruction from Radial Velocities

Reconstructing a three-dimensional velocity field from radial peculiar velocities is non-identifiable because the radial observation operator has a large null space, even when a finite design matrix is formally full rank. Building on Paper I, we construct a potential-flow-constra...

Diffusion Geometry for Galaxy Bulk-Flow Reconstruction I: Foundations and Full-Vector Validation

We formulate a diffusion-spectral field-reconstruction framework based on diffusion geometry for reconstructing cosmological vector fields from finite and nonuniformly distributed galaxy point clouds. Low-frequency eigenfunctions of a Markov operator defined on the point cloud ar...

Diffusion Geometry for Galaxy Bulk-Flow Reconstruction I: Foundations and Full-Vector Validation

We formulate a diffusion-spectral field-reconstruction framework based on diffusion geometry for reconstructing cosmological vector fields from finite and nonuniformly distributed galaxy point clouds. Low-frequency eigenfunctions of a Markov operator defined on the point cloud ar...

Diffusion Geometry for Galaxy Bulk-Flow Reconstruction II: Three-Dimensional Potential-Flow Reconstruction from Radial Velocities

Reconstructing a three-dimensional velocity field from radial peculiar velocities is non-identifiable because the radial observation operator has a large null space, even when a finite design matrix is formally full rank. Building on Paper I, we construct a potential-flow-constra...

A Spatio‐Temporal Neural Kernel Function Model for Earthquake Clustering: Bridging ETAS and Neural Point Processes

Abstract Modeling earthquake clustering in space and time is central to understanding seismicity patterns and improving forecasting. The Epidemic‐Type Aftershock Sequence (ETAS) model, grounded in the Hawkes process, has long been the standard approach for describing aftershock t...