most citedChange-point detection using spectral PCA for multivariate time series

5 citations · 9 across the 7 of their papers we have counts for

collaborators

7 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…

q-bio.NC2021

To Deconvolve, or Not to Deconvolve: Inferences of Neuronal Activities using Calcium Imaging Data

Tong Shen, Gyorgy Lur, Xiangmin Xu +1

With the increasing popularity of calcium imaging data in neuroscience research, methods for analyzing calcium trace data are critical to address various questions. The observed ca…

stat.AP2021

Bayesian nonparametric analysis for the detection of spikes in noisy calcium imaging data

Laura D'Angelo, Antonio Canale, Zhaoxia Yu +1

Recent advancements in miniaturized fluorescence microscopy have made it possible to investigate neuronal responses to external stimuli in awake behaving animals through the analys…

stat.AP2021★ 5 cited

Change-point detection using spectral PCA for multivariate time series

Shuhao Jiao, Tong Shen, Zhaoxia Yu +1

We propose a two-stage approach Spec PC-CP to identify change points in multivariate time series. In the first stage, we obtain a low-dimensional summary of the high-dimensional ti…

stat.ME2017★ 3 cited

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…

stat.ME2017★ 1 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…