| Microbial networks in SPRING-Semi-parametric rank-based correlation and partial correlation estimation for quantitative microbiome data G Yoon, I Gaynanova, CL Müller Frontiers in genetics 10, 449195, 2019 | 88 | 2019 |
| Structural learning and integrative decomposition of multi-view data I Gaynanova, G Li Biometrics 75 (4), 1121-1132, 2019 | 69 | 2019 |
| Sparse semiparametric canonical correlation analysis for data of mixed types G Yoon, RJ Carroll, I Gaynanova Biometrika 107 (3), 609-625, 2020 | 42 | 2020 |
| Oracle inequalities for high-dimensional prediction J Lederer, L Yu, I Gaynanova Bernoulli 25 (2), 1225-1255, 2019 | 40 | 2019 |
| Interpreting blood GLUcose data with R package iglu S Broll, J Urbanek, D Buchanan, E Chun, J Muschelli, NM Punjabi, ... PLoS One 16 (4), e0248560, 2021 | 37 | 2021 |
| Simultaneous Sparse Estimation of Canonical Vectors in the p ≫ N Setting I Gaynanova, JG Booth, MT Wells Journal of the American Statistical Association 111 (514), 696-706, 2016 | 35 | 2016 |
| Correlation of electrophoretic urine protein banding patterns with severity of renal damage in dogs with proteinuric chronic kidney disease JA Hokamp, SA Leidy, I Gaynanova, RE Cianciolo, MB Nabity Veterinary clinical pathology 47 (3), 425-434, 2018 | 34 | 2018 |
| A general framework for association analysis of heterogeneous data G Li, I Gaynanova | 32 | 2018 |
| Non-convex global minimization and false discovery rate control for the TREX J Bien, I Gaynanova, J Lederer, CL Müller Journal of Computational and Graphical Statistics 27 (1), 23-33, 2018 | 23 | 2018 |
| Modeling continuous glucose monitoring (CGM) data during sleep I Gaynanova, N Punjabi, C Crainiceanu Biostatistics 23 (1), 223-239, 2022 | 20 | 2022 |
| Prediction error bounds for linear regression with the TREX J Bien, I Gaynanova, J Lederer, CL Müller Test 28, 451-474, 2019 | 20 | 2019 |
| Sparse quadratic classification rules via linear dimension reduction I Gaynanova, T Wang Journal of multivariate analysis 169, 278-299, 2019 | 17 | 2019 |
| Joint association and classification analysis of multi‐view data Y Zhang, I Gaynanova Biometrics 78 (4), 1614-1625, 2022 | 16 | 2022 |
| Metabolomics of primary cutaneous melanoma and matched adjacent extratumoral microenvironment NJ Taylor, I Gaynanova, SA Eschrich, EA Welsh, TJ Garrett, C Beecher, ... PloS one 15 (10), e0240849, 2020 | 16 | 2020 |
| latentcor: An R Package for estimating latent correlations from mixed data types M Huang, CL Müller, I Gaynanova Journal of Open Source Software 6 (65), 3634, 2021 | 12 | 2021 |
| Prediction and estimation consistency of sparse multi-class penalized optimal scoring I Gaynanova | 12 | 2020 |
| MGSDA: Multi-Group Sparse Discriminant Analysis I Gaynanova R package version 1, 2016 | 9 | 2016 |
| Optimal variable selection in multi-group sparse discriminant analysis I Gaynanova, M Kolar | 9 | 2015 |
| Fast computation of latent correlations G Yoon, CL Müller, I Gaynanova Journal of Computational and Graphical Statistics 30 (4), 1249-1256, 2021 | 8 | 2021 |
| Sparse feature selection in kernel discriminant analysis via optimal scoring AF Lapanowski, I Gaynanova The 22nd International Conference on Artificial Intelligence and Statistics …, 2019 | 8 | 2019 |
Christian L. MüllerHelmholtz Munich; Ludwig-Maximilians-Universität München; Flatiron Institute, New York確認したメール アドレス: helmholtz-munich.de
