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Maria Weese
Maria Weese
Associate Professor of Business Analytics, Miami University
Verified email at miamioh.edu - Homepage
Title
Cited by
Cited by
Year
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
1002016
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
342017
Searching for powerful supersaturated designs
ML Weese, BJ Smucker, DJ Edwards
Journal of Quality Technology 47 (1), 66-84, 2015
252015
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
142017
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
132018
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
112021
Self-validated ensemble models for design of experiments
T Lemkus, C Gotwalt, P Ramsey, ML Weese
Chemometrics and Intelligent Laboratory Systems 219, 104439, 2021
102021
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
102020
Response surface models: To reduce or not to reduce?
BJ Smucker, DJ Edwards, ML Weese
Journal of Quality Technology 53 (2), 197-216, 2021
72021
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
62017
"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
52010
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
42014
Optimal Supersaturated Designs for Lasso Sign Recovery
JW Stallrich, K Young, ML Weese, BJ Smucker, DJ Edwards
arXiv preprint arXiv:2303.16843, 2023
12023
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
12022
Predictive Response Surface Models: To Reduce or Not to Reduce?
B Smucker, DJ Edwards, ML Weese
12018
A Graphical Comparison of Screening Designs using Support Recovery Probabilities
K Young, ML Weese, JW Stallrich, BJ Smucker, DJ Edwards
arXiv preprint arXiv:2311.12685, 2023
2023
Boundary Peeling: Outlier Detection Method Using One-Class Peeling
S Arafat, N Sun, ML Weese, WG Martinez
arXiv preprint arXiv:2309.05630, 2023
2023
one-class peeling for outlier detection in high dimensions
M Weese
2018
Sequential experimentation for mixtures
ML Weese, MG Leitnaker
International Journal of Experimental Design and Process Optimisation 3 (1 …, 2012
2012
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