| Analysis of microbiome data in the presence of excess zeros A Kaul, S Mandal, O Davidov, SD Peddada Frontiers in microbiology 8, 2114, 2017 | 280 | 2017 |
| Structural zeros in high-dimensional data with applications to microbiome studies A Kaul, O Davidov, SD Peddada Biostatistics 18 (3), 422-433, 2017 | 39 | 2017 |
| An Efficient Two Step Algorithm for High Dimensional Change Point Regression Models Without Grid Search. A Kaul, VK Jandhyala, SB Fotopoulos J. Mach. Learn. Res. 20, 111:1-111:40, 2019 | 37 | 2019 |
| Household composition and the infant fecal microbiome: The INSPIRE study AA Lane, MK McGuire, MA McGuire, JE Williams, KA Lackey, EH Hagen, ... American journal of physical anthropology 169 (3), 526-539, 2019 | 35 | 2019 |
| Pivotal estimation via self-normalization for high-dimensional linear models with error in variables A Belloni, A Kaul, M Rosenbaum arXiv preprint arXiv:1708.08353, 2017 | 22 | 2017 |
| Confidence bands for coefficients in high dimensional linear models with error-in-variables A Belloni, V Chernozhukov, A Kaul arXiv preprint arXiv:1703.00469, 2017 | 18 | 2017 |
| Weighted ℓ1-penalized corrected quantile regression for high dimensional measurement error models A Kaul, HL Koul Journal of Multivariate Analysis 140, 72-91, 2015 | 18 | 2015 |
| Inference on the change point under a high dimensional sparse mean shift A Kaul, SB Fotopoulos, VK Jandhyala, A Safikhani Electronic Journal of Statistics 15 (1), 71-134, 2021 | 14 | 2021 |
| Lasso with long memory regression errors A Kaul Journal of Statistical Planning and Inference 153, 11-26, 2014 | 13 | 2014 |
| Detection and estimation of parameters in high dimensional multiple change point regression models via regularization and discrete optimization A Kaul, VK Jandhyala, SB Fotopoulos arXiv preprint arXiv:1906.04396, 2019 | 12 | 2019 |
| Analysis of microbiome data in the presence of excess zeros. Front Microbiol 8: 2114 A Kaul, S Mandal, O Davidov, SD Peddada | 12 | 2017 |
| Two stage non-penalized corrected least squares for high dimensional linear models with measurement error or missing covariates A Kaul, HL Koul, A Chawla, SN Lahiri arXiv preprint arXiv:1605.03154, 2016 | 9 | 2016 |
| Analysis of microbiome data in the presence of excess zeros. Front Microbiol. 2017; 8: 2114 A Kaul, S Mandal, O Davidov, SD Peddada Epub 2017/11/23. https://doi. org/10.3389/fmicb. 2017.02114 PMID: 29163406, 2017 | 6 | 2017 |
| Inference on the Change Point under a High Dimensional Covariance Shift A Kaul, H Zhang, K Tsampourakis, G Michailidis Journal of Machine Learning Research 24 (168), 1-68, 2023 | 5* | 2023 |
| Inference for change points in high dimensional mean shift models A Kaul, G Michailidis arXiv preprint arXiv:2107.09150, 2021 | 5 | 2021 |
| Multiple change‐point models for time series IB MacNeill, VK Jandhyala, A Kaul, SB Fotopoulos Environmetrics, e2593, 2019 | 5 | 2019 |
| Oral microbiome and associations with chemical exposure, asthma and lung function R Bertelsen, T Ringel-Kulka, S Peddada, A Kaul, FG Real, C Svanes European Respiratory Journal 50 (suppl 61), 2017 | 5 | 2017 |
| Segmentation of high dimensional means over multi-dimensional change points and connections to regression trees A Kaul arXiv preprint arXiv:2105.10017, 2021 | 2 | 2021 |
| Inference on Multiple Change Points in High Dimensional Linear Regression Models H Zhang, A Kaul Econometrics and Statistics, 2024 | | 2024 |
| Adaptive parametric change point inference under covariance structure changes SB Fotopoulos, A Kaul, V Pavlopoulos, VK Jandhyala Statistical Papers, 1-27, 2023 | | 2023 |
Soumendra LahiriWashington University in Saint LouisVerified email at wustl.edu
