3 papers
stat.ME2025
Bias Correction in Factor-Augmented Regression Models with Weak Factors
Peiyun Jiang, Yoshimasa Uematsu, Takashi Yamagata
In this paper, we study the asymptotic bias of the factor-augmented regression estimator and its reduction, which is augmented by the factors extracted from a large number of $…
stat.ME2024
Discovering the Network Granger Causality in Large Vector Autoregressive Models
Yoshimasa Uematsu, Takashi Yamagata
This paper proposes novel inferential procedures for discovering the network Granger causality in high-dimensional vector autoregressive models. In particular, we mainly offer two…
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…