| Regularized estimation in sparse high-dimensional time series models S Basu, G Michailidis The Annals of Statistics 43 (4), 1535-1567, 2015 | 518 | 2015 |
| Iterative random forests to discover predictive and stable high-order interactions S Basu, K Kumbier, JB Brown, B Yu Proceedings of the National Academy of Sciences 115 (8), 1943-1948, 2018 | 344 | 2018 |
| Sparse network modeling and metscape-based visualization methods for the analysis of large-scale metabolomics data S Basu, W Duren, CR Evans, CF Burant, G Michailidis, A Karnovsky Bioinformatics 33 (10), 1545-1553, 2017 | 209 | 2017 |
| Network granger causality with inherent grouping structure S Basu, A Shojaie, G Michailidis The Journal of Machine Learning Research 16 (1), 417-453, 2015 | 135 | 2015 |
| A debiased MDI feature importance measure for random forests X Li, Y Wang, S Basu, K Kumbier, B Yu arXiv preprint arXiv:1906.10845, 2019 | 112 | 2019 |
| Low rank and structured modeling of high-dimensional vector autoregressions S Basu, X Li, G Michailidis IEEE Transactions on Signal Processing 67 (5), 1207-1222, 2019 | 84 | 2019 |
| Random forests for spatially dependent data A Saha, S Basu, A Datta Journal of the American Statistical Association, 1-19, 2021 | 71 | 2021 |
| Metabolomic profiling identifies biochemical pathways associated with castration-resistant prostate cancer AK Kaushik, SK Vareed, S Basu, V Putluri, N Putluri, K Panzitt, ... Journal of proteome research 13 (2), 1088-1100, 2014 | 71 | 2014 |
| A high-dimensional approach to measure connectivity in the financial sector S Basu, S Das, G Michailidis, A Purnanandam The Annals of Applied Statistics 18 (2), 922-945, 2024 | 47* | 2024 |
| Metabolic coessentiality mapping identifies C12orf49 as a regulator of SREBP processing and cholesterol metabolism EC Bayraktar, K La, K Karpman, G Unlu, C Ozerdem, DJ Ritter, ... Nature metabolism 2 (6), 487-498, 2020 | 43 | 2020 |
| High-Dimensional Estimation, Basis Assets, and the Adaptive Multi-Factor Model L Zhu, S Basu, RA Jarrow, MT Wells Quarterly Journal of Finance 10 (04), 2050017, 2020 | 34 | 2020 |
| Adaptive thresholding for reconstructing regulatory networks from time-course gene expression data A Shojaie, S Basu, G Michailidis Statistics in Biosciences 4 (1), 66-83, 2012 | 32 | 2012 |
| Methionine-homocysteine pathway in African-American prostate cancer JH Gohlke, SM Lloyd, S Basu, V Putluri, SK Vareed, U Rasaily, ... JNCI cancer spectrum 3 (2), pkz019, 2019 | 27 | 2019 |
| Large spectral density matrix estimation by thresholding Y Sun, Y Li, A Kuceyeski, S Basu arXiv preprint arXiv:1812.00532, 2018 | 27 | 2018 |
| Refining interaction search through signed iterative random forests K Kumbier, S Basu, JB Brown, S Celniker, B Yu arXiv preprint arXiv:1810.07287, 2018 | 26 | 2018 |
| Dense time-course gene expression profiling of the Drosophila melanogaster innate immune response F Schlamp, SYN Delbare, AM Early, MT Wells, S Basu, AG Clark BMC genomics 22, 1-22, 2021 | 23 | 2021 |
| Sparse identification and estimation of large-scale vector autoregressive moving averages I Wilms, S Basu, J Bien, DS Matteson Journal of the American Statistical Association, 1-33, 2021 | 23 | 2021 |
| Exploiting regulatory heterogeneity to systematically identify enhancers with high accuracy H Arbel, S Basu, WW Fisher, AS Hammonds, KH Wan, S Park, ... Proceedings of the National Academy of Sciences 116 (3), 900-908, 2019 | 23 | 2019 |
| Interpretable vector autoregressions with exogenous time series I Wilms, S Basu, J Bien, DS Matteson arXiv preprint arXiv:1711.03623, 2017 | 13 | 2017 |
| Estimation in high-dimensional vector autoregressive models S Basu, G Michailidis arXiv preprint arXiv:1311.4175, 2013 | 13 | 2013 |
George MichailidisProfessor of Statistics and Computer Science在 ufl.edu 的电子邮件经过验证
