Factorial Sampling Plans for Preliminary Computational Experiments
Abstract
A computational model is a representation of some physical or other system of interest, first expressed mathematically and then implemented in the form of a computer program; it may be viewed as a function of inputs that, when evaluated, produces outputs. Motivation for this article comes from computational models that are deterministic, complicated enough to make classical mathematical analysis impractical and that have a moderate-to-large number of inputs. The problem of designing computational experiments to determine which inputs have important effects on an output is considered. The proposed experimental plans are composed of individually randomized one-factor-at-a-time designs, and data analysis is based on the resulting random sample of observed elementary effects, those changes in an output due solely to changes in a particular input. Advantages of this approach include a lack of reliance on assumptions of relative sparsity of important inputs, monotonicity of outputs with respect to inputs, or adequacy of a low-order polynomial as an approximation to the computational model.
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Morris screening identified seven parameters with the highest Morris index with respect to maximum cortisol (Bmax), associated with elimination or production of epinephrine, norepinephrine, and cortisol.
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- Authors
- Morris, M. D.
- Affiliations
- Oak Ridge National Laboratory (US)
- Corresponding author
- Morris, Max D.
- ORCID
- Morris, Max D.
- Venue
- Technometrics
- Publisher
- Informa UK Limited
- Published
- 1991-05
- Volume
- 33
- Issue
- 2
- Pages
- 161-174
- ISSN
- 0040-1706 (ISSN-L)
- 1537-2723
- OpenAlex
- W999207820
- Cited by
- 4,943
- References
- 0
- Citations by year
- 2026: 413 · 2025: 399 · 2024: 374 · 2023: 389 · 2022: 391 · 2021: 364 · 2020: 310 · 2019: 305 · 2018: 317 · 2017: 223 · 2016: 225 · 2015: 225 · 2014: 195 · 2013: 163 · 2012: 166
- FWCI
- 3.32
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Technometrics (publishedVersion)
- Topics
- Social Sciences › Decision Sciences › Management Science and Operations Research › Optimal Experimental Design Methods
- Social Sciences › Decision Sciences › Management Science and Operations Research › Simulation Techniques and Applications
- Social Sciences › Decision Sciences › Statistics, Probability and Uncertainty › Probabilistic and Robust Engineering Design
- Keywords
- numerical experiments, elementary effects, one-factor-at-a-time design, sensitivity analysis, input-output relationship, experimental design
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