Research
Funding Acknowledgements
NSF grants DMS-2311399, DMS-1848579, and DMS-1513653.
Preprints
Liu, M., Zhou, D., Chen, H.
Generalized independence test for modern data.
arXiv:2409.07745
Lin, X., Chen, H.
UBSea: A unified community detection framework.
arXiv:2310.04934
Zhu, Y., Chen, H.
A new robust graph for graph-based methods.
arXiv:2307.15205
Mo, X., Chen, H.
A new classification framework for high-dimensional data.
arXiv:2306.15199
Chen, H., Lin, X.
A new clustering framework.
arXiv:2305.00578
Chu, L., Chen, H.
On the tightness of graph-based statistics.
arXiv:2303.00136
Zhou, D., Chen, H.
Asymptotic distribution-free change-point detection for modern data based on a new ranking scheme.
arXiv:2206.03038
Song, H., Chen, H.
New graph-based multi-sample tests for high-dimensional and non-Euclidean data.
arXiv:2205.13787
Song, H., Chen, H.
A fast and effective large-scale two-sample test based on kernels.
arXiv:2110.03118
Zhang, Y., Chen, H.
Graph-based multiple change-point detection.
arXiv:2110.01170
Zhang, J., Chen, H., Zhou, X.
A new non-parametric test for multivariate paired data and pair matching.
arXiv:2007.01497.
Chen, H., Chen, S., Deng, X.
A universal nonparametric event detection framework for Neuropixels data.
bioRxiv:650671
Chen, H.
Change-point detection for multivariate and non-Euclidean data with local dependency.
arXiv:1903.01598.
Publications
Song, H., Chen, H. (2024)
Practical and powerful kernel-based change-point detection.
IEEE Transactions on Signal Processing, DOI:10.1109/TSP.2024.3479274. R package: kerSeg
Song, H., Chen, H. (2024)
Generalized kernel two-sample tests.
Biometrika, 111(3):755-770. R package: kerTests
Zhu, Y., Chen, H. (2024)
Limiting distributions of graph-based test statistics on sparse and dense graphs. [PDF]
Bernoulli, 30(1):770-796.
Zhou, D., Chen, H. (2023)
A new ranking scheme for modern data and its application to two-sample hypothesis testing.
Proceedings of Thirty Sixth Conference on Learning Theory, PMLR 195:3615-3668
Hu, S., Huang, J., Chen, H., Chan, H. (2023)
Likelihood scores for sparse signal and change-point detection.
IEEE Transactions on Information Theory, 69(6):4065-4080.
Chen, H., Chu, L. (2023)
Graph-based change-point analysis.
Annual Review of Statistics and Its Application, 10:475-499.
Chen, H., Xia, Y. (2023).
A normality test for high-dimensional data based on a nearest neighbor approach.
JASA, Theory and Methods, 118(541):719-731.
Chu, L., Chen, H. (2022)
Sequential change-point detection for high-dimensional and non-Euclidean data.
IEEE Transactions on Signal Processing, 70:4498-4511. R package: gStream
Song, H., Chen, H. (2022)
Asymptotic distribution-free change-point detection for data with repeated observations.
Biometrika, 109(3):783-798. R package: gSeg
Liu, Y., Chen, H. (2022)
A fast and efficient change-point detection framework based on approximate k-nearest neighbor graphs.
IEEE Transactions on Signal Processing, 70:1976-1986.
Chen, H., Small, D. (2022).
New multivariate tests for assessing covariate balance in matched observational studies.
Biometrics, 78:202–213. R package: BalanceCheck
Zhang, J., Chen, H. (2022).
Graph-based two-sample tests for data with repeated observations.
Statistica Sinica, 32:391-415. R package: gTests
Fu, P., Schoenball, M., Ajo-Franklin, J., Chai, C., Maceira, M., Morris, J., Wu, H., Knox, H., Schwering, P., White, M., Burghardt, J., Strickland, C., Johnson, T., Vermeul, V., Sprinkle, P., Roberts, B., Ulrich, C., Guglielmi, Y., Cook, P., Dobson, P., Wood, T., Frash, L., Huang, L., Ingraham, M., Pope, J., Smith, M., Neupane, G., Doe, T., Roggenthen. W., Horne, R., Singh, A., Zoback, M., Wang, H., Condon, K., Ghassemi, A., Chen, H., McClure, M., Vandine, G., Blankenship, D., Kneafsey, T., EGS Collab Team. (2021).
Close observation of hydraulic fracturing at EGS collab experiment 1: fracture trajectory, microseismic interpretations, and the role of natural fractures.
Journal of Geophysical Research - Solid Earth, 126(7), e2020JB020840.
Zhang, Q., Mahdi, G., Tinker, J., Chen, H. (2020).
A graph-based multi-sample test for identifying pathways associated with cancer progression.
Computational Biology and Chemistry, 87:107285.
Chen, H. (2019).
Sequential change-point detection based on nearest neighbors.
The Annals of Statistics, 47(3):1381-1407. R package: gStream
Chu, L., Chen, H. (2019).
Asymptotic distribution-free change-point detection for multivariate and non-Euclidean data.
The Annals of Statistics, 47(1):382-414. R package: gSeg
Chen, H., Chen, X. and Su, Y. (2018).
A weighted edge-count two sample test for multivariate and object data.
JASA, Theory and Methods, 113(523):1146-1155. R package: gTests
Urrutia, E., Chen, H., Zhou, Z., Zhang, N. and Jiang, Y. (2018).
Integrative pipeline for profiling DNA copy number and inferring tumor phylogeny.
Bioinformatics, bty057. R pipeline: Marathon
Maxwell, K., Wubbenhorst, B., Wenz, B., Sloover, D., Pluta, J., Emery, L., Barrett, A., Kraya, A., Anastopoulos, I., Yu, S., Jiang, Y., Chen, H., Zhang, N., Hackman, N., D'Andrea, K., Daber, R., Morrissette, J., Mitra, N., Feldman, M., Domchek, S. and Nathanson, K. L. (2017).
BRCA locus-specific loss of heterozygosity in germline BRCA1 and BRCA2 carriers.
Nature Communications, 8:319.
Chen, H., Jiang, Y., Maxwell, K., Nathanson, K. and Zhang, N. (2017).
Allele-specific copy number estimation by whole Exome sequencing.
The Annals of Applied Statistics, 11(2), 1169. R package: falconx
Wang, X., Chen, H., Zhang, N. (2017).
DNA copy number profiling using single cell sequencing.
Briefings in Bioinformatics, bbx004. R pipeline: SCNV
Chen, H. and Friedman, J. H. (2017).
A new graph-based two-sample test for multivariate and object data.
JASA, Theory and Methods, 112(517):397-409. R package: gTests
Wang, X., Chen, M., Yu, X., Pornputtapong, N., Chen, H., Zhang, N. R., Powers, S. and Krauthammer, M. (2016).
Global copy number profiling of cancer genomes.
Bioinformatics, 32(6):926-928.
Chen, H. and Zhang, N. R. (2015).
Graph-based change-point detection.
The Annals of Statistics, 43(1):139-176. R package: gSeg
Chen, H., Bell, J. M., Zavala, N. A., Ji, H. P. and Zhang, N. R. (2014).
Allele-specific copy number profiling by next-generation DNA sequencing.
Nucleic Acids Research, 43(4):e23. R package: falcon
Nadauld, L. D., Garcia, S., Natsoulis, G., Bell, J. M., Miotke, L., Hopmans, E. S., Xu, H., Pai, R. K., Palm, C., Regan, J. F., Chen, H., Flaherty, P., Ootani, A., Zhang, N. R., Ford, J. M., Kuo, C. J. and Ji, H. P. (2014).
Metastatic tumor evolution and organoid modeling implicate TGFBR2 as a cancer driver in diffuse gastric cancer.
Genome Biology 15:428.
Chen, H. and Zhang, N. R. (2013).
Graph-based tests for two-sample comparisons of categorical data.
Statistica Sinica 23:1479-1503. R package: gCat
Chen, H., Xing, H. and Zhang, N. R. (2011).
Estimation of parent specific DNA copy number in tumors using high-density genotyping arrays.
PLoS Computational Biology 7(1):e1001060
Note: In the author list, underlined are students under my supervision.
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