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Computational Statistics and Data Science (CSDS) lab --- PI: Julia Hua Fang
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Using propensity score modeling to minimize the influence of confounding risks related to prenatal tobacco exposure.
Data Interpretation, Statistical
Pattern Recognition of Longitudinal Trial Data with Nonignorable Missingness: An Empirical Case Study.
A survey of big data research.
Multiple Imputation based Clustering Validation (MIV) for Big Longitudinal Trial Data with Missing Values in eHealth.
A New MI-Based Visualization Aided Validation Index for Mining Big Longitudinal Web Trial Data.
ESammon: A Computationaly Enhanced Sammon Mapping based on Data Density.
A New Mining Method to Detect Real Time Substance Use Events from Wearable Biosensor Data Stream.
An Enhanced Visualization Method to Aid Behavioral Trajectory Pattern Recognition Infrastructure for Big Longitudinal Data.
MIFuzzy Clustering for Incomplete Longitudinal Data in Smart Health.
Multiple- vs Non- or Single-Imputation based Fuzzy Clustering for Incomplete Longitudinal Behavioral Intervention Data.
eFCM: An Enhanced Fuzzy C-Means Algorithm for Longitudinal Intervention Data.
Topic modeling for systematic review of visual analytics in incomplete longitudinal behavioral trial data.
Harmonizing Wearable Biosensor Data Streams to Test Polysubstance Detection.