activity
20062022
most citedMultilevel functional principal component analysis

289 citations · 307 across the 11 of their papers we have counts for

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8 papers · 1 filter

stat.AP2021

Identifying brain hierarchical structures associated with Alzheimer's disease using a regularized regression method with tree predictors

Yi Zhao, Bingkai Wang, Chin-Fu Liu +4

Brain segmentation at different levels is generally represented as hierarchical trees. Brain regional atrophy at specific levels was found to be marginally associated with Alzheime…

stat.AP20192 cited

Template Independent Component Analysis: Targeted and Reliable Estimation of Subject-level Brain Networks using Big Data Population Priors

Amanda F. Mejia, Mary Beth Nebel, Yikai Wang +2

Large brain imaging databases contain a wealth of information on brain organization in the populations they target, and on individual variability. While such databases have been us…

stat.AP2018

Sparse Principal Component based High-Dimensional Mediation Analysis

Yi Zhao, Martin A. Lindquist, Brian S. Caffo

Causal mediation analysis aims to quantify the intermediate effect of a mediator on the causal pathway from treatment to outcome. With multiple mediators, which are potentially cau…

stat.AP2018

Functional Mediation Analysis with an Application to Functional Magnetic Resonance Imaging Data

Yi Zhao, Xi Luo, Martin Lindquist +1

Causal mediation analysis is widely utilized to separate the causal effect of treatment into its direct effect on the outcome and its indirect effect through an intermediate variab…

stat.AP20131 cited

Parametrization of white matter manifold-like structures using principal surfaces

Chen Yue, Vadim Zipunnikov, Pierre-Louis Bazin +4

In this manuscript, we are concerned with data generated from a diffusion tensor imaging (DTI) experiment. The goal is to parameterize manifold-like white matter tracts, such as th…

stat.AP2013

Homotopic Group ICA for Multi-Subject Brain Imaging Data

Juemin Yang, Ani Eloyan, Anita Barber +5

Independent Component Analysis (ICA) is a computational technique for revealing latent factors that underlie sets of measurements or signals. It has become a standard technique in…