36 citations · 88 across the 12 of their papers we have counts for
6 papers · 1 filter
A joint model of the individual mean and within-subject variability of a longitudinal outcome with a competing risks time-to-event outcome
Shanpeng Li, Daniel S. Nuyujukian, Robyn L. McClelland +4
Motivated by a growing body of research emphasizing the importance of modeling within-subject (WS) variability in longitudinal biomarkers and its association with health outcomes,…
Efficient Algorithms and Implementation of a Semiparametric Joint Model for Longitudinal and Competing Risks Data: With Applications to Massive Biobank Data
Shanpeng Li, Ning Li, Hong Wang +3
Semiparametric joint models of longitudinal and competing risks data are computationally costly and their current implementations do not scale well to massive biobank data. This pa…
Matrix Linear Discriminant Analysis
Wei Hu, Weining Shen, Hua Zhou +1
We propose a novel linear discriminant analysis approach for the classification of high-dimensional matrix-valued data that commonly arises from imaging studies. Motivated by the e…
Provable Convex Co-clustering of Tensors
Eric C. Chi, Brian R. Gaines, Will Wei Sun +2
Cluster analysis is a fundamental tool for pattern discovery of complex heterogeneous data. Prevalent clustering methods mainly focus on vector or matrix-variate data and are not a…
Tensor Generalized Estimating Equations for Longitudinal Imaging Analysis
Xiang Zhang, Lexin Li, Hua Zhou +2
In an increasing number of neuroimaging studies, brain images, which are in the form of multidimensional arrays (tensors), have been collected on multiple subjects at multiple time…
Tucker Tensor Regression and Neuroimaging Analysis
Xiaoshan Li, Hua Zhou, Lexin Li
Large-scale neuroimaging studies have been collecting brain images of study individuals, which take the form of two-dimensional, three-dimensional, or higher dimensional arrays, al…