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pro vyhledávání: '"Chen, Yiqun T."'
Autor:
Chen, Yiqun T., Witten, Daniela M.
We consider the problem of testing for a difference in means between clusters of observations identified via k-means clustering. In this setting, classical hypothesis tests lead to an inflated Type I error rate. To overcome this problem, we take a se
Externí odkaz:
http://arxiv.org/abs/2203.15267
The graph fused lasso -- which includes as a special case the one-dimensional fused lasso -- is widely used to reconstruct signals that are piecewise constant on a graph, meaning that nodes connected by an edge tend to have identical values. We consi
Externí odkaz:
http://arxiv.org/abs/2109.10451
In recent years, a number of methods have been proposed to estimate the times at which a neuron spikes on the basis of calcium imaging data. However, quantifying the uncertainty associated with these estimated spikes remains an open problem. We consi
Externí odkaz:
http://arxiv.org/abs/2103.07818
Autor:
Chen, Yiqun T.1 YIQUNC@STANFORD.EDU, Witten, Daniela M.2 DWITTEN@UW.EDU
Publikováno v:
Journal of Machine Learning Research. 2023, Vol. 24, p1-20. 41p.
Publikováno v:
Biostatistics; Apr2023, Vol. 24 Issue 2, p481-501, 21p
Akademický článek
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Autor:
Veseli I, Chen YT, Schechter MS, Vanni C, Fogarty EC, Watson AR, Jabri BA, Blekhman R, Willis AD, Yu MK, Fernandez-Guerra A, Fussel J, Eren AM
Publikováno v:
BioRxiv : the preprint server for biology [bioRxiv] 2024 Jul 26. Date of Electronic Publication: 2024 Jul 26.