Estimation of Grouped Time-Varying Network Vector Autoregressive Models

Degui Li, Bin Peng, Songqiao Tang et al.

On Non-asymptotic Theory of Recurrent Neural Networks in Temporal Point Processes

Zhiheng Chen, Guanhua Fang, Wen Yu

Temporal point process (TPP) is an important tool for modeling and predicting irregularly timed events across various domains. Recently, the recurrent...

Dynamic Bayesian Learning for Spatiotemporal Mechanistic Models

Sudipto Banerjee, Xiang Chen, Ian Frankenburg et al.

We develop an approach for Bayesian learning of spatiotemporal dynamical mechanistic models. Such learning consists of statistical emulation of the me...

Simultaneous inference for monotone and smoothly time-varying functions under complex temporal dynamics

Tianpai Luo, Weichi Wu

Optimal Vintage Factor Analysis with Deflation Varimax

Xin Bing, Xin He, Dian Jin et al.

Identifying the Structure of High-Dimensional Time Series via Eigen-Analysis

Bo Zhang, Jiti Gao, Guangming Pan et al.

Design and analysis of randomized trials to estimate spatio-temporally heterogeneous treatment effects

Samuel I. Watson, Thomas A. Smith

Frequency Domain Statistical Inference for High-Dimensional Time Series

Jonas Krampe, Efstathios Paparoditis

High-Dimensional Knockoffs Inference for Time Series Data

Yingying Fan, Jinchi Lv, Chien-Ming Chi et al.

Discovering the Network Granger Causality in Large Vector Autoregressive Models

Yoshimasa Uematsu, Takashi Yamagata

On the Modeling and Prediction of High-Dimensional Functional Time Series

Jinyuan Chang, Qiwei Yao, Xinghao Qiao et al.