Kehui Chen
University of Pittsburgh
38 Papers
72 Citations
Kehui Chen is an academic researcher from University of Pittsburgh. The author has contributed to research in topics: Functional data analysis & Stochastic block model. The author has an hindex of 16, co-authored 33 publications. Previous affiliations of Kehui Chen include University of California, Davis & Carnegie Mellon University.
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Papers
Network Cross-Validation for Determining the Number of Communities in Network Data
Kehui Chen,Jing Lei +1 more
TL;DR: In this paper, the authors developed an efficient network cross-validation (NCV) approach to determine the number of communities, as well as to choose between the regular stochastic block model and the degree corrected block model (DCBM).
193
Distinct Physiological Maturation of Parvalbumin-Positive Neuron Subtypes in Mouse Prefrontal Cortex.
TL;DR: The maturation of ChCs and BCs in different layers of the mouse PFC was assessed, and it was found that, from early postnatal age, ChCs or BCs differ in laminar location, and may contribute to the emergence of cognitive function differentially, and predominantly during prepubertal development.
131
Conditional quantile analysis when covariates are functions, with application to growth data
Kehui Chen,Hans-Georg Müller +1 more
TL;DR: In this paper, a method for conditional quantile analysis when predictors take values in a functional space is proposed, which facilitates balancing of model flexibility and the curse of dimensionality for the infinite dimensional functional predictors.
Consistent community detection in multi-layer network data
Jing Lei,Kehui Chen,Brian Lynch +2 more
TL;DR: The theorems show that, as compared to single-layer community detection, a multi-layer network provides much richer information that allows for consistent community detection from a much sparser network, with required edge density reduced by a factor of the square root of the number of layers.
86
Localized Functional Principal Component Analysis
Kehui Chen,Jing Lei +1 more
TL;DR: It is proved that the proposed LFPCA converges to the original functional principal component analysis (FPCA) when the tuning parameters are chosen appropriately and can almost perfectly recover the true eigenfunctions and significantly improve the estimation accuracy when the eigenFunctions are truly supported on some subdomains.