100 citations · 204 across the 32 of their papers we have counts for
10 papers · 1 filter
Adaptive variational Bayes: Optimality, computation and applications
Ilsang Ohn, Lizhen Lin
In this paper, we explore adaptive inference based on variational Bayes. Although several studies have been conducted to analyze the contraction properties of variational posterior…
Optimal Bayesian estimation of Gaussian mixtures with growing number of components
Ilsang Ohn, Lizhen Lin
We study Bayesian estimation of finite mixture models in a general setup where the number of components is unknown and allowed to grow with the sample size. An assumption on growin…
Differential Geometry for Model Independent Analysis of Images and Other Non-Euclidean Data: Recent Developments
Rabi Bhattacharya, Lizhen Lin
This article provides an exposition of recent methodologies for nonparametric analysis of digital observations on images and other non-Euclidean objects. Fréchet means of distribut…
Averages of Unlabeled Networks: Geometric Characterization and Asymptotic Behavior
Eric Kolaczyk, Lizhen Lin, Steven Rosenberg +2
It is becoming increasingly common to see large collections of network data objects -- that is, data sets in which a network is viewed as a fundamental unit of observation. As a re…
Bayesian Sparse Linear Regression with Unknown Symmetric Error
Minwoo Chae, Lizhen Lin, David B. Dunson
We study full Bayesian procedures for sparse linear regression when errors have a symmetric but otherwise unknown distribution. The unknown error distribution is endowed with a sym…
On Posterior Consistency of Tail Index for Bayesian Kernel Mixture Models
Cheng Li, Lizhen Lin, David B. Dunson
Asymptotic theory of tail index estimation has been studied extensively in the frequentist literature on extreme values, but rarely in the Bayesian context. We investigate whether…
Extrinsic local regression on manifold-valued data
Lizhen Lin, Brian St. Thomas, Hongtu Zhu +1
We propose an extrinsic regression framework for modeling data with manifold valued responses and Euclidean predictors. Regression with manifold responses has wide applications in…
Learning Subspaces of Different Dimension
Brian St. Thomas, Lizhen Lin, Lek-Heng Lim +1
We introduce a Bayesian model for inferring mixtures of subspaces of different dimensions. The key challenge in such a mixture model is specification of prior distributions over su…
Robust and Scalable Bayes via a Median of Subset Posterior Measures
Stanislav Minsker, Sanvesh Srivastava, Lizhen Lin +1
We propose a novel approach to Bayesian analysis that is provably robust to outliers in the data and often has computational advantages over standard methods. Our technique is base…
Omnibus CLTs for Fréchet means and nonparametric inference on non-Euclidean spaces
Rabi Bhattacharya, Lizhen Lin
Two central limit theorems for sample Fréchet means are derived, both significant for nonparametric inference on non-Euclidean spaces. The first one, Theorem 2.2, encompasses and i…