59 citations · 124 across the 8 of their papers we have counts for
7 papers · 1 filter
One-step group factor analysis via penalized least squares
Xinbing Kong, Xiaoying Pan, Long Yu +1
In this article, we revisit the problem of group factor analysis and propose a one-step penalized least squares method to estimate the factor loadings and factors in large-dimensio…
Robust Statistical Inference for Large-dimensional Matrix-valued Time Series via Iterative Huber Regression
Yong He, Xin-Bing Kong, Dong Liu +1
Matrix factor model is drawing growing attention for simultaneous two-way dimension reduction of well-structured matrix-valued observations. This paper focuses on robust statistica…
Matrix Quantile Factor Model
Xin-Bing Kong, Yong-Xin Liu, Long Yu +1
This paper introduces a matrix quantile factor model for matrix-valued data with low-rank structure. We estimate the row and column factor spaces via minimizing the empirical check…
Online Change-point Detection for Matrix-valued Time Series with Latent Two-way Factor Structure
Yong He, Xin-bing Kong, Lorenzo Trapani +1
This paper proposes a novel methodology for the online detection of changepoints in the factor structure of large matrix time series. Our approach is based on the well-known fact t…
One-way or Two-way Factor Model for Matrix Sequences?
Yong He, Xin-bing Kong, Lorenzo Trapani +1
This paper investigates the issue of determining the dimensions of row and column factor spaces in matrix-valued data. Exploiting the eigen-gap in the spectrum of sample second mom…
Projected Estimation for Large-dimensional Matrix Factor Models
Long Yu, Yong He, Xin-bing Kong +1
In this study, we propose a projection estimation method for large-dimensional matrix factor models with cross-sectionally spiked eigenvalues. By projecting the observation matrix…
Large-dimensional Factor Analysis without Moment Constraints
Yong He, Xinbing Kong, Long Yu +1
Large-dimensional factor model has drawn much attention in the big-data era, in order to reduce the dimensionality and extract underlying features using a few latent common factors…