| Statistical learning methods applied to process monitoring: An overview and perspective M Weese, W Martinez, FM Megahed, LA Jones-Farmer Journal of Quality Technology 48 (1), 4-24, 2016 | 107 | 2016 |
| Response surface experiments: A meta-analysis RA Ockuly, ML Weese, BJ Smucker, DJ Edwards, L Chang Chemometrics and Intelligent Laboratory Systems 164, 64-75, 2017 | 36 | 2017 |
| Searching for powerful supersaturated designs ML Weese, BJ Smucker, DJ Edwards Journal of Quality Technology 47 (1), 66-84, 2015 | 27 | 2015 |
| A criterion for constructing powerful supersaturated designs when effect directions are known ML Weese, DJ Edwards, BJ Smucker Journal of Quality Technology 49 (3), 265-277, 2017 | 16 | 2017 |
| Strategies for Supersaturated Screening: Group Orthogonal and Constrained Var(s) Designs ML Weese, JW Stallrich, BJ Smucker, DJ Edwards Technometrics 63 (4), 443-455, 2021 | 14 | 2021 |
| Analysis of definitive screening designs: Screening vs prediction ML Weese, PJ Ramsey, DC Montgomery Applied Stochastic Models in Business and Industry 34 (2), 244-255, 2018 | 13 | 2018 |
| Self-validated ensemble models for design of experiments T Lemkus, C Gotwalt, P Ramsey, ML Weese Chemometrics and Intelligent Laboratory Systems 219, 104439, 2021 | 12 | 2021 |
| A one‐class peeling method for multivariate outlier detection with applications in phase I SPC WG Martinez, ML Weese, LA Jones-Farmer Quality and Reliability Engineering International 36 (4), 1272-1295, 2020 | 10 | 2020 |
| Response surface models: To reduce or not to reduce? BJ Smucker, DJ Edwards, ML Weese Journal of Quality Technology 53 (2), 197-216, 2021 | 7 | 2021 |
| Compositional models and organizational research: Application of a mixture model to nonexperimental data in the context of CEO Pay JT Campbell, ML Weese Organizational Research Methods 20 (1), 95-120, 2017 | 6 | 2017 |
| "On the selection of the Bandwidth Parameter for the k-chart" LA Weese, M.L., Martinez, W.G., Jones-Farmer Quality and Reliability Engineering International, 2016 | 6* | 2016 |
| A new screening methodology for mixture experiments M Weese | 5 | 2010 |
| Comparing methods for design follow‐up: revisiting a metal‐cutting case study DJ Edwards, ML Weese, GA Palmer Applied Stochastic Models in Business and Industry 30 (4), 464-478, 2014 | 4 | 2014 |
| Optimal Supersaturated Designs for Lasso Sign Recovery JW Stallrich, K Young, ML Weese, BJ Smucker, DJ Edwards arXiv preprint arXiv:2303.16843, 2023 | 3 | 2023 |
| A graphical comparison of screening designs using support recovery probabilities K Young, ML Weese, JW Stallrich, BJ Smucker, DJ Edwards Journal of Quality Technology, 1-14, 2024 | 1 | 2024 |
| Machine Learning Models Identify Inhibitors of New Delhi Metallo-β-lactamase Z Cheng, M Aitha, CA Thomas, A Sturgill, M Fairweather, A Hu, CR Bethel, ... Journal of chemical information and modeling, 2024 | 1 | 2024 |
| An Optimal Design Framework for Lasso Sign Recovery JW Stallrich, K Young, ML Weese, BJ Smucker, DJ Edwards arXiv preprint arXiv:2303.16843, 2023 | 1 | 2023 |
| Robustness of the one‐class Peeling method to the Gaussian Kernel Bandwidth L Lee, ML Weese, WG Martinez, LA Jones‐Farmer Quality and Reliability Engineering International 38 (3), 1289-1301, 2022 | 1 | 2022 |
| Predictive Response Surface Models: To Reduce or Not to Reduce? B Smucker, DJ Edwards, ML Weese | 1 | 2018 |
| Large Row-Constrained Supersaturated Designs for High-throughput Screening BJ Smucker, SE Wright, I Williams, RC Page, AJ Kiss, SB Silwal, M Weese, ... arXiv preprint arXiv:2407.06173, 2024 | | 2024 |
Byran SmuckerAssociate Professor of Statistics, Miami University, Oxford, OH在 miamioh.edu 的电子邮件经过验证
