| Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States EY Cramer, EL Ray, VK Lopez, J Bracher, A Brennen, ... Proceedings of the National Academy of Sciences 119 (15), e2113561119, 2022 | 423* | 2022 |
| Does increased sexual frequency enhance happiness? G Loewenstein, T Krishnamurti, J Kopsic, D Mcdonald Journal of Economic Behavior & Organization 116, 206-218, 2015 | 94 | 2015 |
| An open repository of real-time COVID-19 indicators A Reinhart, L Brooks, M Jahja, A Rumack, J Tang, S Agrawal, W Al Saeed, ... Proceedings of the National Academy of Sciences 118 (51), e2111452118, 2021 | 92 | 2021 |
| A primer on the mortgage market and mortgage finance DJ McDonald, DL Thornton Review-Federal Reserve Bank of Saint Louis 90 (1), 31, 2008 | 92 | 2008 |
| Evaluation of FluSight influenza forecasting in the 2021–22 and 2022–23 seasons with a new target laboratory-confirmed influenza hospitalizations SM Mathis, AE Webber, TM León, EL Murray, M Sun, LA White, LC Brooks, ... Nature communications 15 (1), 6289, 2024 | 64 | 2024 |
| The lasso, persistence, and cross-validation D Homrighausen, D McDonald International conference on machine learning, 1031-1039, 2013 | 60 | 2013 |
| Can auxiliary indicators improve COVID-19 forecasting and hotspot prediction? DJ McDonald, J Bien, A Green, AJ Hu, N DeFries, S Hyun, NL Oliveira, ... Proceedings of the National Academy of Sciences 118 (51), e2111453118, 2021 | 59 | 2021 |
| Leave-one-out cross-validation is risk consistent for lasso D Homrighausen, DJ McDonald Machine learning 97 (1), 65-78, 2014 | 53 | 2014 |
| The impact of price discounts and calorie messaging on beverage consumption: a multi-site field study JJS Jue, MJ Press, D McDonald, KG Volpp, DA Asch, N Mitra, ... Preventive medicine 55 (6), 629-633, 2012 | 51 | 2012 |
| Nonparametric risk bounds for time-series forecasting DJ McDonald, CR Shalizi, M Schervish Journal of Machine Learning Research 18 (32), 1-40, 2017 | 50* | 2017 |
| Risk-consistency of cross-validation with lasso-type procedures D Homrighausen, DJ McDonald Statistica Sinica 27 (3), 1017-1036, 2017 | 45 | 2017 |
| Estimating beta-mixing coefficients D Mcdonald, C Shalizi, M Schervish Proceedings of the Fourteenth International Conference on Artificial …, 2011 | 45 | 2011 |
| Estimating beta-mixing coefficients via histograms DJ McDonald, CR Shalizi, M Schervish Electronic Journal of Statistics 9 (2), 2855-2883, 2015 | 38 | 2015 |
| Less is more: balancing noise reduction and data retention in fMRI with data-driven scrubbing DĐ Phạm, DJ McDonald, L Ding, MB Nebel, AF Mejia NeuroImage 270, 119972, 2023 | 33* | 2023 |
| sparsegl: An r package for estimating sparse group lasso X Liang, A Cohen, AS Heinsfeld, F Pestilli, DJ McDonald Journal of Statistical Software 110, 1-23, 2024 | 22 | 2024 |
| Rademacher Complexity of Stationary Sequences DJ McDonald, CR Shalizi arXiv preprint arXiv:1106.0730, 2017 | 22 | 2017 |
| On the Nyström and column-sampling methods for the approximate principal components analysis of large datasets D Homrighausen, DJ McDonald Journal of Computational and Graphical Statistics 25 (2), 344-362, 2016 | 18 | 2016 |
| Flexible analysis of TSS mapping data and detection of TSS shifts with TSRexploreR RA Policastro, DJ McDonald, VP Brendel, GE Zentner NAR Genomics and Bioinformatics 3 (2), lqab051, 2021 | 17 | 2021 |
| Minimax Density Estimation for Growing Dimension DJ McDonald AISTATS 54, 194-203, 2017 | 16 | 2017 |
| A study on tuning parameter selection for the high-dimensional lasso D Homrighausen, DJ McDonald Journal of Statistical Computation and Simulation 88 (15), 2865-2892, 2018 | 15 | 2018 |
Cosma Rohilla ShaliziProfessor of Statistics, Carnegie Mellon UniversityVerified email at cmu.edu
