| rqPen: penalized quantile regression B Sherwood, A Maidman R package version 2 (2), 2020 | 34 | 2020 |
| Central nervous system injury–a newly observed bystander effect of radiation C Feiock, M Yagi, A Maidman, A Rendahl, S Hui, D Seelig PloS one 11 (9), e0163233, 2016 | 32 | 2016 |
| Wild residual bootstrap inference for penalized quantile regression with heteroscedastic errors L Wang, I Van Keilegom, A Maidman Biometrika 105 (4), 859-872, 2018 | 22 | 2018 |
| Package ‘rqPen’ B Sherwood, A Maidman, MB Sherwood, T ByteCompile R foundation for statistical computing, 2017 | 12 | 2017 |
| New semiparametric method for predicting high‐cost patients A Maidman, L Wang Biometrics 74 (3), 1104-1111, 2018 | 10 | 2018 |
| rqPen: penalized quantile regression. R package version 2.2. 2 B Sherwood, A Maidman | 6 | 2020 |
| rqPen: Penalized Quantile Regression, 2016 B Sherwood, A Maidman URL https://cran. rproject. org/web/packages/rqPen. R package version, 1-4, 0 | 5 | |
| Additive nonlinear quantile regression in ultra-high dimension B Sherwood, A Maidman Journal of Machine Learning Research 23 (63), 1-47, 2022 | 2 | 2022 |
| Kernel intensity estimation of 2-dimensional spatial poisson point processes from k-tree sampling AM Ellison, NJ Gotelli, N Hsiang, M Lavine, AB Maidman Journal of agricultural, biological, and environmental statistics 19 (3 …, 2014 | 2 | 2014 |
| Quantile partially linear additive model for data with dropouts and an application to modeling cognitive decline A Maidman, L Wang, XH Zhou, B Sherwood Statistics in Medicine 42 (16), 2729-2745, 2023 | 1 | 2023 |
| Semiparametric Quantile Regression and Applications to Healthcare Data Analysis A Maidman university of minnesota, 2018 | | 2018 |
| Package ‘plaqr’ A Maidman | | 2017 |
| Relaxing the Linearity Condition in Discovering Semiparametric Forms A Maidman | | 2015 |
| Deep Learning: Developing an R package and addressing open questions in the MNIST applications A Maidman, A Molstad, Y Yang, L Zhang | | 2015 |
| Deep Learning A Maidman, A Molstad, Y Yang, L Zhang | | 2015 |
| Supplementary material for wild residual bootstrap inference for penalized quantile regression with heteroscedastic errors L Wang, I Van Keilegom, A Maidman | | |
| k-tree density estimation from sparse nearest-neighbor data AM Ellison, NJ Gotelli, N Hsiang, AB Maidman, M Lavine | | |
