E-LIB Suche - Ergebnisse für: Witten, Daniela M

1

Modeling functional cell types in spike train data:

Zdeblick, Daniel N. ; Shea-Brown, Eric T. ; Witten, Daniela M...
PLOS Computational Biology. 19 (2023) 10 - p. e1011509 , 2023

Link: https://doi.org/10.1371/..

2

Inferring spikes from calcium imaging in dopamine neurons:

Fleming, Weston ; Jewell, Sean ; Engelhard, Ben...
PLOS ONE. 16 (2021) 6 - p. e0252345 , 2021

Link: https://doi.org/10.1371/..

3

Quantifying uncertainty in spikes estimated from calcium im..:

Chen, Yiqun T ; Jewell, Sean W ; Witten, Daniela M
Biostatistics. 24 (2021) 2 - p. 481-501 , 2021

Link: https://doi.org/10.1093/..

4

Adaptive nonparametric regression with the K-nearest neighb..:

Madrid Padilla, Oscar Hernan ; Sharpnack, James ; Chen, Yanzhen.
Biometrika. 107 (2020) 2 - p. 293-310 , 2020

Link: https://doi.org/10.1093/..

5

Multi‐scale network regression for brain‐phenotype associat..:

Xia, Cedric Huchuan ; Ma, Zongming ; Cui, Zaixu...
Human Brain Mapping. 41 (2020) 10 - p. 2553-2566 , 2020

Link: https://doi.org/10.1002/..

6

Fast nonconvex deconvolution of calcium imaging data:

Jewell, Sean W ; Hocking, Toby Dylan ; Fearnhead, Paul.
Biostatistics. 21 (2019) 4 - p. 709-726 , 2019

Link: https://doi.org/10.1093/..

7

Corrigendum: A systematic comparison reveals substantial di..:

Inoue, Fumitaka ; Kircher, Martin ; Martin, Beth...
Genome Research. 28 (2018) 5 - p. 766.3-766.3 , 2018

Link: https://doi.org/10.1101/..

8

Network Reconstruction From High-Dimensional Ordinary Diffe..:

Chen, Shizhe ; Shojaie, Ali ; Witten, Daniela M.
Journal of the American Statistical Association. 112 (2017) 520 - p. 1697-1707 , 2017

Link: https://doi.org/10.1080/..

9

Network Reconstruction From High-Dimensional Ordinary Diffe..:

Chen, Shizhe ; Shojaie, Ali ; Witten, Daniela M.
Journal of the American Statistical Association. 112 (2017) 520 - p. 1697-1707 , 2017

Link: https://www.jstor.org/st..

10

Replicates in high dimensions, with applications to latent ..:

TAN, KEAN MING ; NING, YANG ; WITTEN, DANIELA M..
Biometrika. 103 (2016) 4 - p. 761-777 , 2016

Link: https://www.jstor.org/st..

11

A systematic comparison reveals substantial differences in ..:

Inoue, Fumitaka ; Kircher, Martin ; Martin, Beth...
Genome Research. 27 (2016) 1 - p. 38-52 , 2016

Link: https://doi.org/10.1101/..

12

Replicates in high dimensions, with applications to latent ..:

Tan, Kean Ming ; Ning, Yang ; Witten, Daniela M..
Biometrika. 103 (2016) 4 - p. 761-777 , 2016

Link: https://doi.org/10.1093/..

13

Selection and estimation for mixed graphical models:

CHEN, SHIZHE ; WITTEN, DANIELA M. ; SHOJAIE, ALI
Biometrika. 102 (2015) 1 - p. 47-64 , 2015

Link: https://www.jstor.org/st..

14

Sparse Biclustering of Transposable Data:

Tan, Kean Ming ; Witten, Daniela M.
Journal of Computational and Graphical Statistics. 23 (2014) 4 - p. 985-1008 , 2014

Link: https://www.jstor.org/st..

15

The joint graphical lasso for inverse covariance estimation..:

Danaher, Patrick ; Wang, Pei ; Witten, Daniela M.
Journal of the Royal Statistical Society. Series B (Statistical Methodology). 76 (2014) 2 - p. 373-397 , 2014

Link: http://www.jstor.org/sta..