10 citations · 12 across the 8 of their papers we have counts for
9 papers
Revisiting Penalized Likelihood Estimation for Deterministic Computer Experiments
Ayumi Mutoh, Annie S. Booth, Jonathan W. Stallrich
Gaussian processes (GPs) are popular as nonlinear regression models for expensive computer simulations, yet GP performance relies heavily on estimation of unknown covariance parame…
Powerful Foldover Designs
Jonathan W. Stallrich, Rakhi Singh, Kyle Vogt-Lowell +1
The foldover technique for screening designs is well known to guarantee zero aliasing of the main effect estimators with respect to two factor interactions and quadratic effects. I…
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…
Optimal Designs for Two-Stage Inference
Jonathan W. Stallrich, Michael McKibben
The analysis of screening experiments is often done in two stages, starting with factor selection via an analysis under a main effects model. The success of this first stage is inf…
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…
Statistical Challenges in Online Controlled Experiments: A Review of A/B Testing Methodology
Nicholas Larsen, Jonathan Stallrich, Srijan Sengupta +3
The rise of internet-based services and products in the late 1990's brought about an unprecedented opportunity for online businesses to engage in large scale data-driven decision m…
D- and A-optimal Screening Designs
Jonathan Stallrich, Katherine Allen-Moyer, Bradley Jones
In practice, optimal screening designs for arbitrary run sizes are traditionally generated using the D-criterion with factor settings fixed at +/- 1, even when considering continuo…
Tuning Parameter Selection for Penalized Estimation via
Julia Holter, Jonathan Stallrich
The tuning parameter selection strategy for penalized estimation is crucial to identify a model that is both interpretable and predictive. However, popular strategies (e.g., minimi…
Sequential Optimization in Locally Important Dimensions
Munir A. Winkel, Jonathan W. Stallings, Curt B. Storlie +1
Optimizing an expensive, black-box function is challenging when its input space is high-dimensional. Sequential design frameworks first model with a surrogate…