activity
20172021
most citedStatistical Challenges in Modeling Big Brain Signals

1 citations · 1 across the 4 of their papers we have counts for

collaborators

6 papers

stat.ME2021

Ridge-penalized adaptive Mantel test and its application in imaging genetics

Dustin Pluta, Tong Shen, Gui Xue +3

We propose a ridge-penalized adaptive Mantel test (AdaMant) for evaluating the association of two high-dimensional sets of features. By introducing a ridge penalty, AdaMant tests t…

stat.ME2021

A Measurement of In-Betweenness and Inference Based on Shape Theories

Dustin Pluta, Xiangmin Xu, Daniel L. Gillen +1

We propose a statistical framework to investigate whether a given subpopulation lies between two other subpopulations in a multivariate feature space. This methodology is motivated…

stat.ML2020

Conjoined Dirichlet Process

Michelle N. Ngo, Dustin S. Pluta, Alexander N. Ngo +1

Biclustering is a class of techniques that simultaneously clusters the rows and columns of a matrix to sort heterogeneous data into homogeneous blocks. Although many algorithms hav…

stat.ME2019

Modeling Dynamic Functional Connectivity with Latent Factor Gaussian Processes

Lingge Li, Dustin Pluta, Babak Shahbaba +3

Dynamic functional connectivity, as measured by the time-varying covariance of neurological signals, is believed to play an important role in many aspects of cognition. While many…

stat.AP2018

Topological Brain Network Distances

Moo K. Chung, Hyekyoung Lee, Andrey Gritsenko +4

Existing brain network distances are often based on matrix norms. The element-wise differences in the existing matrix norms may fail to capture underlying topological differences.…

stat.ME20171 cited

Statistical Challenges in Modeling Big Brain Signals

Zhaoxia Yu, Dustin Pluta, Tong Shen +3

Brain signal data are inherently big: massive in amount, complex in structure, and high in dimensions. These characteristics impose great challenges for statistical inference and l…