6 papers
Bayesian Structured Mediation Analysis With Unobserved Confounders
Yuliang Xu, Shu Yang, Jian Kang
We explore methods to reduce the impact of unobserved confounders on the causal mediation analysis of high-dimensional mediators with spatially smooth structures, such as brain ima…
A tree-based kernel for densities and its applications in clustering DNase-seq profiles
Yuliang Xu, Kaixuan Luo, Li Ma
Modeling multiple sampling densities within a hierarchical framework enables borrowing of information across samples. These density random effects can act as kernels in latent vari…
Two-sample comparison through additive tree models for density ratios
Naoki Awaya, Yuliang Xu, Li Ma
The ratio of two densities provides a direct characterization of their differences. We consider the two-sample comparison problem by estimating this ratio given i.i.d. observations…
Scalable Bayesian Image-on-Scalar Regression for Population-Scale Neuroimaging Data Analysis
Yuliang Xu, Timothy D. Johnson, Thomas E. Nichols +1
Bayesian Image-on-Scalar Regression (ISR) provides flexible, uncertainty-aware neuroimaging analysis. However, applying ISR to large-scale datasets such as the UK Biobank is challe…
Bayesian Image Mediation Analysis
Yuliang Xu, Timothy D Johnson, Mary Heitzeg +1
Mediation analysis aims to separate the indirect effect through mediators from the direct effect of the exposure on the outcome. It is challenging to perform mediation analysis wit…
Distributional Evaluation of Generative Models via Relative Density Ratio
Yuliang Xu, Yun Wei, Li Ma
We propose a function-valued evaluation metric for generative models based on the relative density ratio (RDR) designed to characterize distributional differences between real and…