most citedTucker tensor factor models: matricization and mode-wise PCA estimation

1 citations · 1 across the 7 of their papers we have counts for

collaborators

7 papers

stat.ME2022

Empirical likelihood-based portmanteau tests for autoregressive moving average models with possible infinite variance innovation

Xiaohui Liu, Donghui Fan, Xu Zhang +1

It is an important task in the literature to check whether a fitted autoregressive moving average (ARMA) model is adequate, while the currently used tests may suffer from the size…

stat.ME2022

Non-segmental Bayesian Detection of Multiple Change-points

Chong Zhong, Zhihua Ma, Xu Zhang +1

We propose an original and general NOn-SEgmental (NOSE) approach for the detection of multiple change-points. NOSE identifies change-points by the non-negligibility of posterior es…

stat.ME2022

Modeling and Learning on High-Dimensional Matrix-Variate Sequences

Xu Zhang, Catherine C. Liu, Jianhua Guo +2

We propose a new matrix factor model, named RaDFaM, which is strictly derived based on the general rank decomposition and assumes a structure of a high-dimensional vector factor mo…

stat.ME2022★ 1 cited

Tucker tensor factor models: matricization and mode-wise PCA estimation

Xu Zhang, Guodong Li, Catherine C. Liu +1

High-dimensional, higher-order tensor data are gaining prominence in a variety of fields, including but not limited to computer vision and network analysis. Tensor factor models, i…

stat.ME2022

Bayesian prediction via nonparametric transformation models

Chong Zhong, Jin Yang, Junshan Shen +2

This article tackles the old problem of prediction via a nonparametric transformation model (NTM) in a new Bayesian way. Estimation of NTMs is known challenging due to model uniden…

stat.AP2022

Low-rank Latent Matrix-factor Prediction Modeling for Generalized High-dimensional Matrix-variate Regression

Yuzhe Zhang, Xu Zhang, Hong Zhang +2

Motivated by diagnosing the COVID-19 disease using 2D image biomarkers from computed tomography (CT) scans, we propose a novel latent matrix-factor regression model to predict resp…

stat.ME2021

Dependent Dirichlet Processes for Analysis of a Generalized Shared Frailty Model

Chong Zhong, Zhihua Ma, Junshan Shen +1

Bayesian paradigm takes advantage of well fitting complicated survival models and feasible computing in survival analysis owing to the superiority in tackling the complex censoring…