1 citations · 2 across the 5 of their papers we have counts for
6 papers
The use of cross validation in the analysis of designed experiments
Maria L. Weese, Byran J. Smucker, David J. Edwards
Cross-validation (CV) is a common method to tune machine learning methods and can be used for model selection in regression as well. Because of the structured nature of small, trad…
Large Row-Constrained Supersaturated Designs for High-throughput Screening
Byran J. Smucker, Stephen E. Wright, Isaac Williams +5
High-throughput screening, in which multiwell plates are used to test large numbers of compounds against specific targets, is widely used across many areas of the biological scienc…
A Graphical Comparison of Screening Designs using Support Recovery Probabilities
Kade Young, Maria L. Weese, Jonathan W. Stallrich +2
A screening experiment attempts to identify a subset of important effects using a relatively small number of experimental runs. Given the limited run size and a large number of pos…
Boundary Peeling: Outlier Detection Method Using One-Class Peeling
Sheikh Arafat, Na Sun, Maria L. Weese +1
Unsupervised outlier detection constitutes a crucial phase within data analysis and remains a dynamic realm of research. A good outlier detection algorithm should be computationall…
An Optimal Design Framework for Lasso Sign Recovery
Jonathan W. Stallrich, Kade Young, Maria L. Weese +2
Supersaturated designs investigate more factors than there are runs, and are often constructed under a criterion measuring a design's proximity to an unattainable orthogonal design…
Self-Validated Ensemble Models for Design of Experiments
Trent Lemkus, Philip Ramsey, Christopher Gotwalt +1
One of the possible objectives when designing experiments is to build or formulate a model for predicting future observations. When the primary objective is prediction, some typica…