7 citations · 22 across the 13 of their papers we have counts for
13 papers
Functional Bayesian Networks for Discovering Causality from Multivariate Functional Data
Fangting Zhou, Kejun He, Kunbo Wang +2
Multivariate functional data arise in a wide range of applications. One fundamental task is to understand the causal relationships among these functional objects of interest, which…
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…
A Bayesian Nonparametric Approach for Inferring Drug Combination Effects on Mental Health in People with HIV
Wei Jin, Yang Ni, Leah H. Rubin +2
Although combination antiretroviral therapy (ART) is highly effective in suppressing viral load for people with HIV (PWH), many ART agents may exacerbate central nervous system (CN…
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…
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…
A Nonparametric Bayesian Basket Trial Design
Yanxun Xu, Peter Mueller, Apostolia M Tsimberidou +1
Targeted therapies on the basis of genomic aberrations analysis of the tumor have shown promising results in cancer prognosis and treatment. Regardless of tumor type, trials that m…