2 citations · 7 across the 8 of their papers we have counts for
4 papers · 1 filter
Revisiting Asymptotic Theory for Principal Component Estimators of Approximate Factor Models
Peiyun Jiang, Yoshimasa Uematsu, Takashi Yamagata
It is well known that approximate factor models exhibit rotation indeterminacy. Principal component (PC) estimators are typically analyzed relative to a rotated factor-loading repr…
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…
IPAD: Stable Interpretable Forecasting with Knockoffs Inference
Yingying Fan, Jinchi Lv, Mahrad Sharifvaghefi +1
Interpretability and stability are two important features that are desired in many contemporary big data applications arising in economics and finance. While the former is enjoyed…
Penalized Likelihood Estimation in High-Dimensional Time Series Models and its Application
Yoshimasa Uematsu
This paper presents a general theoretical framework of penalized quasi-maximum likelihood (PQML) estimation in stationary multiple time series models when the number of parameters…