5 citations · 12 across the 7 of their papers we have counts for
7 papers
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…
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…
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…
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…
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.…
Adaptive Mantel Test for AssociationTesting in Imaging Genetics Data
Dustin Pluta, Hernando Ombao, Chuansheng Chen +3
Mantel's test (MT) for association is conducted by testing the linear relationship of similarity of all pairs of subjects between two observational domains. Motivated by applicatio…
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…