Curtis B. Storlie

Works (7)

Updated: July 5th, 2023 15:54

2020 article

Sequential Optimization in Locally Important Dimensions

Winkel, M. A., Stallrich, J. W., Storlie, C. B., & Reich, B. J. (2020, January 14). Technometrics, Vol. 2, pp. 1–20.

By: M. Winkel n, J. Stallrich n , C. Storlie* & B. Reich n 

Contributors: M. Winkel n, J. Stallrich n , C. Storlie* & B. Reich n 

author keywords: Augmented expected improvement; Bayesian analysis; Computer experiments; Gaussian process; Local importance; Sequential design
topics (OpenAlex): Advanced Multi-Objective Optimization Algorithms; Machine Learning and Data Classification; Metaheuristic Optimization Algorithms Research
TL;DR: A sequential design algorithm called sequential optimization in locally important dimensions (SOLID) is developed that incorporates global and local variable selection to optimize a continuous, differentiable function. (via Semantic Scholar)
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Sources: Web Of Science, NC State University Libraries, ORCID
Added: March 16, 2020

2012 journal article

Variable selection for high dimensional Bayesian density estimation: application to human exposure simulation

Journal of the Royal Statistical Society. Series C, Applied Statistics, 61, 47–66.

By: B. Reich , E. Kalendra, C. Storlie, H. Bondell & M. Fuentes

Source: NC State University Libraries
Added: August 6, 2018

2011 journal article

Surface estimation, variable selection, and the nonparametric oracle property

Statistica Sinica, 21(2), 679–705.

By: C. Storlie, H. Bondell, B. Reich  & H. Zhang

Source: NC State University Libraries
Added: August 6, 2018

2010 article

A Locally Adaptive Penalty for Estimation of Functions With Varying Roughness

Storlie, C. B., Bondell, H. D., & Reich, B. J. (2010, January 1). Journal of Computational and Graphical Statistics, Vol. 19, pp. 569–589.

By: C. Storlie*, H. Bondell n & B. Reich n 

Contributors: C. Storlie*, H. Bondell n & B. Reich n 

author keywords: L-Spline; Local bandwidth; Nonparametric regression; Regularization method; Spatially adaptive smoothing; SS-ANOVA
topics (OpenAlex): Advanced Numerical Analysis Techniques; Image and Signal Denoising Methods; Model Reduction and Neural Networks
TL;DR: The Loco-Spline substantially outperforms the traditional smoothing spline and the locally adaptive kernel smoother and achieves optimal MSE rate of convergence in a simulation study. (via Semantic Scholar)
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Sources: Web Of Science, NC State University Libraries, ORCID
Added: August 6, 2018

2008 review

Multiple predictor smoothing methods for sensitivity analysis: Description of techniques

[Review of ]. Reliability Engineering & System Safety, 93(1), 28–54.

By: C. Storlie & J. Helton

Source: NC State University Libraries
Added: August 6, 2018

2007 article

A sampling-based computational strategy for the representation of epistemic uncertainty in model predictions with evidence theory

Helton, J. C., Johnson, J. D., Oberkampf, W. L., & Storlie, C. B. (2007, March 28). Computer Methods in Applied Mechanics and Engineering.

By: J. Helton*, J. Johnson, W. Oberkampf* & C. Storlie n

author keywords: dempster-shafer theory; epistemic uncertainty; evidence theory; Monte Carlo; numerical uncertainty propagation; sensitivity analysis; uncertainty analysis
topics (OpenAlex): Probabilistic and Robust Engineering Design; Advanced Multi-Objective Optimization Algorithms; Model Reduction and Neural Networks
TL;DR: Preliminary trials indicate that the presented strategy can be used to propagate uncertainty representations based on evidence theory in analysis situations where naive sampling-based (i.e., unsophisticated Monte Carlo) procedures are impracticable due to computational cost. (via Semantic Scholar)
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Source: Web Of Science
Added: August 6, 2018

2007 article

Multiple predictor smoothing methods for sensitivity analysis: Example results

Storlie, C. B., & Helton, J. C. (2007, January 27). Reliability Engineering & System Safety.

By: C. Storlie n & J. Helton*

author keywords: additive models; epistemic uncertainty; locally weighted regression; nonparametric regression; projection pursuit regression; recursive partitioning regression; scatterplot smoothing; sensitivity analysis; stepwise selection; uncertainty analysis
topics (OpenAlex): Probabilistic and Robust Engineering Design; Structural Response to Dynamic Loads; Optimal Experimental Design Methods
TL;DR: Using multiple predictor smoothing methods in sampling-based sensitivity analyses of complex models can yield more informative sensitivity analysis results than can be obtained with more traditional sensitivity analysis procedures based on linear regression, rank regression or quadratic regression when nonlinear relationships between model inputs and model predictions are present. (via Semantic Scholar)
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Source: Web Of Science
Added: August 6, 2018

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