Large-scale and multidimensional spatiotemporal data sets are becoming ubiquitous in
many real-world applications such as monitoring urban traffic and air quality. Making …

The literature suggests that the country in which a company is listed, ie, its country
membership, is the main determinant of its price volatility co-movements in the global stock …

This article considers the estimation and inference of the low-rank components in high-
dimensional matrix-variate factor models, where each dimension of the matrix-variates (p× …

We consider to model matrix time series based on a tensor canonical polyadic (CP)-
decomposition. Instead of using an iterative algorithm which is the standard practice for …

Factor model is an appealing and effective analytic tool for high-dimensional time series,
with a wide range of applications in economics, finance and statistics. This paper develops …

Tensor factor model estimation by iterative projection Page 1 The Annals of Statistics 2024,
Vol. 52, No. 6, 2641–2667 https://doi.org/10.1214/24-AOS2412 © Institute of Mathematical …

Diagnostic procedures for finding outliers in high dimensional multivariate time series and
robust estimation methods for these data are reviewed. First, methods for searching for …

Observations in various applications are frequently represented as a time series of
multidimensional arrays, called tensor time series, preserving the inherent multidimensional …

In this article, we study large-dimensional matrix factor models and estimate the factor
loading matrices and factor score matrix by minimizing square loss function. Interestingly …

This article introduces the functional tensor singular value decomposition (FTSVD), a novel
dimension reduction framework for tensors with one functional mode and several tabular …