activity
20182022
most citedLearned practical guidelines for evaluating Conditional Entropy and Mutual Information in discovering major factors of response-vs-covariate dynamics

7 citations · 17 across the 9 of their papers we have counts for

collaborators

12 papers

stat.AP2022

Unraveling heterogeneity of ADNI's time-to-event data using conditional entropy Part-I: Cross-sectional study

Shuting Liao, Fushing Hsieh

Through Alzheimer's Disease Neuroimaging Initiative (ADNI), time-to-event data: from the pre-dementia state of mild cognitive impairment (MCI) to the diagnosis of Alzheimer's disea…

stat.ME20227 cited

Learned practical guidelines for evaluating Conditional Entropy and Mutual Information in discovering major factors of response-vs-covariate dynamics

Ting-Li Chen, Hsieh Fushing, Elizabeth P. Chou

We reformulate and reframe a series of increasingly complex parametric statistical topics into a framework of response-vs-covariate (Re-Co) dynamics that is described without any e…

stat.ME20221 cited

Multiscale major factor selections for complex system data with structural dependency and heterogeneity

Hsieh Fushing, Elizabeth Chou, Ting-Li Chen

Based on structured data derived from large complex systems, we computationally further develop and refine a major factor selection protocol by accommodating structural dependency…

stat.ME2021

An Encoding Approach for Stable Change Point Detection

Xiaodong Wang, Fushing Hsieh

Without imposing prior distributional knowledge underlying multivariate time series of interest, we propose a nonparametric change-point detection approach to estimate the number o…

stat.ML20213 cited

Coarse- and fine-scale geometric information content of Multiclass Classification and implied Data-driven Intelligence

Fushing Hsieh, Xiaodong Wang

Under any Multiclass Classification (MCC) setting defined by a collection of labeled point-cloud specified by a feature-set, we extract only stochastic partial orderings from all p…

stat.ME20211 cited

Discovering Multiple Phases of Dynamics by Dissecting Multivariate Time Series

Xiaodong Wang, Fushing Hsieh

We proposed a data-driven approach to dissect multivariate time series in order to discover multiple phases underlying dynamics of complex systems. This computing approach is devel…