3 papers
stat.ML2026
Provable FDR Control for Deep Feature Selection: Deep MLPs and Beyond
Kazuma Sawaya
We develop a flexible feature selection framework based on deep neural networks that approximately controls the false discovery rate (FDR), a measure of Type-I error. The method ap…
math.ST2024
Moment-Based Adjustments of Statistical Inference in High-Dimensional Generalized Linear Models
Kazuma Sawaya, Yoshimasa Uematsu, Masaaki Imaizumi
We develop a statistical inference method for generalized linear models (GLMs) in high-dimensional settings, where the number of unknown coefficients is of the same order as th…
math.ST2024
High-Dimensional Single-Index Models: Link Estimation and Marginal Inference
Kazuma Sawaya, Yoshimasa Uematsu, Masaaki Imaizumi
This study proposes a novel method for estimation and hypothesis testing in high-dimensional single-index models. We address a common scenario where the sample size and the dimensi…