Efficient methods for sensitivity analysis

Clemson, Barry; Tang, Yongming; Pyne, James C.; Unal, Resit

doi:10.1002/sdr.4260110104

Cited by 43 publications

(31 citation statements)

References 13 publications

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“…Clemson et al 13 discussed the very small amount of attention given to sensitivity analysis in the SDM literature. In addition to the model-as-solution approach in SDM, the dearth of designed experimentation in its ‡ Kriging was named after D. G. Krige, a South African mining geologist who originally developed a preliminary version of the statistical technique.…”

Section: Deterministic Model Experimentation: Sdmmentioning

confidence: 99%

Using Unreplicated 2k−p Designs for Characterizing Moderately Dimensioned Deterministic Computer Models

Houston

Ferreira

Montgomery

2005

Qual. Reliab. Engng. Int.

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Deterministic computer simulation models (DCSMs) have been used extensively by two very different schools of researchers. One has produced highly dimensioned models (hundreds of parameters) of very complex physical phenomena and has explored various means of sensitivity analysis. The other school typically produces low-dimensioned models (dozens of parameters) and has very limited experience with sensitivity analysis. However, the sophisticated techniques used in the large models are not readily accessible to users of the smaller models. The approaches of both schools are reviewed before describing how classical DOE plans can be applied to sensitivity analysis of moderately dimensioned (a dozen to a couple hundred parameters) DCSMs. Because the usual analysis of classical DOE requires randomness, alternative means of experimental analysis are discussed. Four techniques for DCSM experimental analysis, including per cent contribution to variation, are described and the results are statistically compared.

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Section: Deterministic Model Experimentation: Sdmmentioning

confidence: 99%

Using Unreplicated 2k−p Designs for Characterizing Moderately Dimensioned Deterministic Computer Models

Houston

Ferreira

Montgomery

2005

Qual. Reliab. Engng. Int.

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“…Les paramètres d'entrée des simulations sont déterminés de façon à limiter le nombre de simulations néces-saires à la création du méta-modèle. Ils sont échantillonnés suivant une méthode LHS (Latin Hypercube Sampling) qui permet d'explorer de façon optimisée l'ensemble de la variabilité des paramètres d'entrée [McKay et al, 1979,Sacks et al, 1989,Clemson et al, 1995,Fang et al, 2005.…”

Section: Modélisation Du Houppierunclassified

Improving the Integration of Coppice Forest Protection in Rockfall Model

Toe¹,

Bourrier²,

Berger³

2015

Engineering Geology for Society and Territory - Volume 2

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“…Sensitivity analysis has been thoroughly discussed within risk analysis literature, revealing an extensive and diverse approach to sensitivity analysis including numerous reviews (e.g. Clemson et al [81]; Eschenbach and Gimpel [82]; Hamby [83]; Lomas and Eppel [84]; Tzafestas et al [85]). On the other hand, Panell [86] contends that the existing literature is limited, with the vast majority being highly mathematical and theoretical in nature.…”

Section: Sensitivity Analysismentioning

confidence: 99%

Assessing Uncertainty and Risk in Public Sector Investment Projects

Bock¹,

Trück²

2011

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The feasibility and profitability of large investment projects are frequently subject to a partially or even fully undeterminable future, encompassing uncertainty and various types of risk. We investigate significant issues in the field of project appraisal techniques, including risks and uncertainties, appropriate risk analysis, project duration as well as the dependencies between (sub-) projects. The most common project appraisal techniques are examined addressing benefits and weaknesses of each technique. Furthermore, the practical use of the different techniques for the public sector is examined, exemplifying this with a small-scale analysis of the risk analysis procedures of the World Bank. Our findings suggest that in particular for the public sector, practical implementation of quantitative techniques like Monte Carlo simulation in the appraisal procedure of investment projects has not fully occurred to date. We strongly recommend further application of these approaches to the evaluation of processes and financial or economic risk factors in project appraisal of public sector institutions.

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Efficient methods for sensitivity analysis

Cited by 43 publications

References 13 publications

Using Unreplicated 2k−p Designs for Characterizing Moderately Dimensioned Deterministic Computer Models

Using Unreplicated 2k−p Designs for Characterizing Moderately Dimensioned Deterministic Computer Models

Improving the Integration of Coppice Forest Protection in Rockfall Model

Assessing Uncertainty and Risk in Public Sector Investment Projects

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