7 citations · 27 across the 19 of their papers we have counts for
19 papers
Uncertainty quantification for mixed membership in multilayer networks with degree heterogeneity using Gaussian variational inference
Fangzheng Xie, Hsin-Hsiung Huang
Analyzing multilayer networks is central to understanding complex relational measurements collected across multiple conditions or over time. A pivotal task in this setting is to qu…
SANVI: A Fast Spectral-Assisted Network Variational Inference Method with an Extended Surrogate Likelihood Function
Dingbo Wu, Fangzheng Xie
Bayesian inference has been broadly applied to statistical network analysis, but suffers from the expensive computational costs due to the nature of Markov chain Monte Carlo sampli…
Bayesian Sparse Gaussian Mixture Model in High Dimensions
Dapeng Yao, Fangzheng Xie, Yanxun Xu
We study the sparse high-dimensional Gaussian mixture model when the number of clusters is allowed to grow with the sample size. A minimax lower bound for parameter estimation is e…
Statistical inference of random graphs with a surrogate likelihood function
Dingbo Wu, Fangzheng Xie
Spectral estimators have been broadly applied to statistical network analysis, but they do not incorporate the likelihood information of the network sampling model. This paper prop…
Eigenvector-Assisted Statistical Inference for Signal-Plus-Noise Matrix Models
Fangzheng Xie, Dingbo Wu
In this paper, we develop a generalized Bayesian inference framework for a collection of signal-plus-noise matrix models arising in high-dimensional statistics and many application…
An approximate Bayes factor based high dimensional MANOVA using Random Projections
Roger S Zoh, Fangzheng Xie
High-dimensional mean vector testing problem for two or more groups remain a very active research area. In these setting, traditional tests are not applicable because they involve…
Entrywise limit theorems of eigenvectors for signal-plus-noise matrix models with weak signals
Fangzheng Xie
We establish a finite-sample Berry-Esseen theorem for the entrywise limits of the eigenvectors for a broad collection of signal-plus-noise random matrix models under challenging we…
Euclidean Representation of Low-Rank Matrices and Its Statistical Applications
Fangzheng Xie
Low-rank matrices are pervasive throughout statistics, machine learning, signal processing, optimization, and applied mathematics. In this paper, we propose a novel and user-friend…
Estimating the Penalty Level of -minimization via Two Gaussian Approximation Methods
Fang Xie
In this paper, we aim to give a theoretical approximation for the penalty level of -regularization problems. This can save much time in practice compared with the traditi…
Efficient Estimation for Random Dot Product Graphs via a One-step Procedure
Fangzheng Xie, Yanxun Xu
We propose a one-step procedure to estimate the latent positions in random dot product graphs efficiently. Unlike the classical spectral-based methods such as the adjacency and Lap…
Optimal Bayesian Estimation for Random Dot Product Graphs
Fangzheng Xie, Yanxun Xu
We propose a Bayesian approach, called the posterior spectral embedding, for estimating the latent positions in random dot product graphs, and prove its optimality. Unlike the clas…
BAREB: A Bayesian repulsive biclustering model for periodontal data
Yuliang Li, Dipankar Bandyopadhyay, Fangzheng Xie +1
Preventing periodontal diseases (PD) and maintaining the structure and function of teeth are important goals for personal oral care. To understand the heterogeneity in patients wit…
Bayesian Estimation of Sparse Spiked Covariance Matrices in High Dimensions
Fangzheng Xie, Yanxun Xu, Carey E. Priebe +1
We propose a Bayesian methodology for estimating spiked covariance matrices with jointly sparse structure in high dimensions. The spiked covariance matrix is reparametrized in term…
A theoretical framework of the scaled Gaussian stochastic process in prediction and calibration
Mengyang Gu, Fangzheng Xie, Long Wang
Model calibration or data inversion is one of fundamental tasks in uncertainty quantification. In this work, we study the theoretical properties of the scaled Gaussian stochastic p…
Bayesian Projected Calibration of Computer Models
Fangzheng Xie, Yanxun Xu
We develop a Bayesian approach called Bayesian projected calibration to address the problem of calibrating an imperfect computer model using observational data from a complex physi…
A Theoretical Framework for Bayesian Nonparametric Regression
Fangzheng Xie, Wei Jin, Yanxun Xu
We develop a unifying framework for Bayesian nonparametric regression to study the rates of contraction with respect to the integrated -distance without assuming the regressio…
Adaptive Bayesian nonparametric regression using kernel mixture of polynomials with application to partial linear model
Fangzheng Xie, Yanxun Xu
We propose a kernel mixture of polynomials prior for Bayesian nonparametric regression. The regression function is modeled by local averages of polynomials with kernel mixture weig…
Bayesian Repulsive Gaussian Mixture Model
Fangzheng Xie, Yanxun Xu
We develop a general class of Bayesian repulsive Gaussian mixture models that encourage well-separated clusters, aiming at reducing potentially redundant components produced by ind…
BayCount: A Bayesian Decomposition Method for Inferring Tumor Heterogeneity using RNA-Seq Counts
Fangzheng Xie, Mingyuan Zhou, Yanxun Xu
Tumor is heterogeneous - a tumor sample usually consists of a set of subclones with distinct transcriptional profiles and potentially different degrees of aggressiveness and respon…