52 citations · 55 across the 7 of their papers we have counts for
5 papers · 1 filter
Learning Collapsed Patterns in Compositional Data: A Bayesian Heterogeneous Relative-Shift Approach
Maoran Xu, Guanyu Hu
Relative-shift regression provides a principled framework for modeling compositional covariates by quantifying how the response changes when mass is reallocated from one component…
Identifiable and interpretable nonparametric factor analysis
Maoran Xu, Steven Winter, Amy H. Herring +1
Factor models are widely used to reduce dimensionality in modeling high-dimensional data. However, there remains a need for models that can be reliably fit in modest sample sizes a…
Bayesian Inference using the Proximal Mapping: Uncertainty Quantification under Varying Dimensionality
Maoran Xu, Hua Zhou, Yujie Hu +1
In statistical applications, it is common to encounter parameters supported on a varying or unknown dimensional space. Examples include the fused lasso regression, the matrix recov…
Bayesian Inference with the l1-ball Prior: Solving Combinatorial Problems with Exact Zeros
Maoran Xu, Leo L. Duan
The l1-regularization is very popular in high dimensional statistics -- it changes a combinatorial problem of choosing which subset of the parameter are zero, into a simple continu…
Bayesian Multi-scale Modeling of Factor Matrix without using Partition Tree
Maoran Xu, Leo L. Duan
The multi-scale factor models are particularly appealing for analyzing matrix- or tensor-valued data, due to their adaptiveness to local geometry and intuitive interpretation. Howe…