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Predicting Responses from Weighted Networks with Node Covariates in an Application to Neuroimaging
Daniel Kessler, Keith Levin, Elizaveta Levina
We consider the setting where many networks are observed on a common node set, and each observation comprises edge weights of a network, covariates observed at each node, and an ov…
Latent process models for functional network data
Peter W. MacDonald, Elizaveta Levina, Ji Zhu
Network data are often sampled with auxiliary information or collected through the observation of a complex system over time, leading to multiple network snapshots indexed by a con…
Network resampling for estimating uncertainty
Qianhua Shan, Elizaveta Levina
With network data becoming ubiquitous in many applications, many models and algorithms for network analysis have been proposed. Yet methods for providing uncertainty estimates in a…
Selective Inference for Sparse Multitask Regression with Applications in Neuroimaging
Snigdha Panigrahi, Natasha Stewart, Chandra Sekhar Sripada +1
Multi-task learning is frequently used to model a set of related response variables from the same set of features, improving predictive performance and modeling accuracy relative t…