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Andrea Saltelli - One of the best experts on this subject based on the ideXlab platform.
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Global Sensitivity Analysis: An Introduction
2020Co-Authors: Andrea Saltelli, Stefano Tarantola, F CampolongoAbstract:This presentation aims to introduce global Sensitivity Analysis (SA), targeting an audience unfamiliar with the topic, and to give practical hints about the associated advantages and the effort needed. To this effect, we shall review some techniques for Sensitivity Analysis, including those that are not global, by applying them to a simple example. This will give the audience a chance to contrast each method’s result against the audience’s own expectation of what the Sensitivity pattern for the simple model should be. We shall also try to relate the discourse on the relative importance of model input factors to specific questions, such as “Which of the uncertain input factor(s) is so noninfluential that we can safely fix it/them?” or “If we could eliminate the uncertainty in one of the input factors, which factor should we choose to reduce the most the variance of the output?” In this way, the selection of the method for Sensitivity Analysis will be put in relation to the framing of the Analysis and to the interpretation and presentation of the results. The choice of the output of interest will be discussed in relation to the purpose of the model based Analysis. The main methods that we present in this lecture are all related with one another, and are the method of Morris for factors’ screening and the variancebased measures. All are model-free, in the sense that their application does not rely on special assumptions on the behaviour of the model (such as linearity, monotonicity and additivity of the relationship between input factor and model output). Monte Carlo filtering will be also be discussed to demonstrate the usefulness of global Sensitivity Analysis in relation to estimation.
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Sensitivity Analysis for Hydraulic Models
Journal of Hydraulic Engineering, 2009Co-Authors: Jim W. Hall, Stefano Tarantola, Shawn A. Boyce, Yueling Wang, Richard Dawson, Andrea SaltelliAbstract:Sensitivity Analysis is well recognized as being an important aspect of the responsible use of hydraulic models. This paper reviews a range of methods for Sensitivity Analysis. Two applications, one to a simple pipe bend example and the second to an advanced Shallow Water Equation solver, illustrate the deficiencies of standardized regression coefficients in the context of functionally nonlinear models. Derivatives and other local methods of Sensitivity Analysis are shown to give an incomplete picture of model response over the range of variability in the model inputs. The use of global variance-based Sensitivity Analysis is shown to be more general in its applicability and in its capacity to reflect nonlinear processes and the effects of interactions among variables.
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Global Sensitivity Analysis. The Primer - Introduction to Sensitivity Analysis
Global Sensitivity Analysis. The Primer, 2008Co-Authors: Andrea Saltelli, T Andres, F Campolongo, D Gatelli, Michaela Saisana, Matt Ratto, Jessica Cariboni, Stefano TarantolaAbstract:Sensitivity Analysis provides users of mathematical and simulation models with tools to appreciate the dependency of the model output from model input and to investigate how important is each model input in determining its output. All application areas are concerned, from theoretical physics to engineering and socio-economics. This introductory paper provides the Sensitivity Analysis aims and objectives in order to explain the composition of the overall “Sensitivity Analysis” chapter of the Springer Handbook. It also describes the basic principles of Sensitivity Analysis, some classification grids to understand the application B. Iooss ( ) Industrial Risk Management Department, EDF RD biooss@yahoo.fr A. Saltelli Centre for the Study of the Sciences and the Humanities (SVT), University of Bergen (UIB), Bergen, Norway Institut de Ciencia i Tecnologia Ambientals (ICTA), Universitat Autonoma de Barcelona (UAB), Barcelona, Spain e-mail: andrea.saltelli@svt.uib.no; andrea.saltelli@jrc.ec.europa.eu © Springer International Publishing Switzerland 2015 R. Ghanem et al. (eds.), Handbook of Uncertainty Quantification, DOI 10.1007/978-3-319-11259-6_31-1 1 2 B. Iooss and A. Saltelli ranges of each method, a useful software package, and the notations used in the chapter papers. This section also offers a succinct description of Sensitivity auditing, a new discipline that tests the entire inferential chain including model development, implicit assumptions, and normative issues and which is recommended when the inference provided by the model needs to feed into a regulatory or policy process. For the “Sensitivity Analysis” chapter, in addition to this introduction, eight papers have been written by around twenty practitioners from different fields of application. They cover the most widely used methods for this subject: the deterministic methods as the local Sensitivity Analysis, the experimental design strategies, the sampling-based and variance-based methods developed from the 1980s, and the new importance measures and metamodelbased techniques established and studied since the 2000s. In each paper, toy examples or industrial applications illustrate their relevance and usefulness.
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Global Sensitivity Analysis. The Primer
Global Sensitivity Analysis. The Primer, 2008Co-Authors: Andrea Saltelli, T Andres, F Campolongo, D Gatelli, Michaela Saisana, Matt Ratto, Jessica Cariboni, Stefano TarantolaAbstract:Complex mathematical and computational models are used in all areas of society and technology and yet model based science is increasingly contested or refuted, especially when models are applied to controversial themes in domains such as health, the environment or the economy. More stringent standards of proofs are demanded from model-based numbers, especially when these numbers represent potential financial losses, threats to human health or the state of the environment. Quantitative Sensitivity Analysis is generally agreed to be one such standard. Mathematical models are good at mapping assumptions into inferences. A modeller makes assumptions about laws pertaining to the system, about its status and a plethora of other, often arcane, system variables and internal model settings. To what extent can we rely on the model-based inference when most of these assumptions are fraught with uncertainties? Global Sensitivity Analysis offers an accessible treatment of such problems via quantitative Sensitivity Analysis, beginning with the first principles and guiding the reader through the full range of recommended practices with a rich set of solved exercises. The text explains the motivation for Sensitivity Analysis, reviews the required statistical concepts, and provides a guide to potential applications. The book: Provides a self-contained treatment of the subject, allowing readers to learn and practice global Sensitivity Analysis without further materials. Presents ways to frame the Analysis, interpret its results, and avoid potential pitfalls. Features numerous exercises and solved problems to help illustrate the applications. Is authored by leading Sensitivity Analysis practitioners, combining a range of disciplinary backgrounds. Postgraduate students and practitioners in a wide range of subjects, including statistics, mathematics, engineering, physics, chemistry, environmental sciences, biology, toxicology, actuarial sciences, and econometrics will find much of use here. This book will prove equally valuable to engineers working on risk Analysis and to financial analysts concerned with pricing and hedging. © 2008 John Wiley & Sons, Ltd.
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Sensitivity Analysis for importance assessment
Risk Analysis, 2002Co-Authors: Andrea SaltelliAbstract:We review briefly some examples that would support an extended role for quantitative Sensitivity Analysis in the context of model-based Analysis (Section 1). We then review what features a quantitative Sensitivity Analysis needs to have to play such a role (Section 2). The methods that meet these requirements are described in Section 3; an example is provided in Section 4. Some pointers to further research are set out in Section 5.
A. Philip Dawid - One of the best experts on this subject based on the ideXlab platform.
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Probabilistic Sensitivity Analysis in health economics.
Statistical methods in medical research, 2011Co-Authors: Gianluca Baio, A. Philip DawidAbstract:Health economic evaluations have recently become an important part of the clinical and medical research process and have built upon more advanced statistical decision-theoretic foundations. In some contexts, it is officially required that uncertainty about both parameters and observable variables be properly taken into account, increasingly often by means of Bayesian methods. Among these, probabilistic Sensitivity Analysis has assumed a predominant role. The objective of this article is to review the problem of health economic assessment from the standpoint of Bayesian statistical decision theory with particular attention to the philosophy underlying the procedures for Sensitivity Analysis.
Stefano Tarantola - One of the best experts on this subject based on the ideXlab platform.
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Global Sensitivity Analysis: An Introduction
2020Co-Authors: Andrea Saltelli, Stefano Tarantola, F CampolongoAbstract:This presentation aims to introduce global Sensitivity Analysis (SA), targeting an audience unfamiliar with the topic, and to give practical hints about the associated advantages and the effort needed. To this effect, we shall review some techniques for Sensitivity Analysis, including those that are not global, by applying them to a simple example. This will give the audience a chance to contrast each method’s result against the audience’s own expectation of what the Sensitivity pattern for the simple model should be. We shall also try to relate the discourse on the relative importance of model input factors to specific questions, such as “Which of the uncertain input factor(s) is so noninfluential that we can safely fix it/them?” or “If we could eliminate the uncertainty in one of the input factors, which factor should we choose to reduce the most the variance of the output?” In this way, the selection of the method for Sensitivity Analysis will be put in relation to the framing of the Analysis and to the interpretation and presentation of the results. The choice of the output of interest will be discussed in relation to the purpose of the model based Analysis. The main methods that we present in this lecture are all related with one another, and are the method of Morris for factors’ screening and the variancebased measures. All are model-free, in the sense that their application does not rely on special assumptions on the behaviour of the model (such as linearity, monotonicity and additivity of the relationship between input factor and model output). Monte Carlo filtering will be also be discussed to demonstrate the usefulness of global Sensitivity Analysis in relation to estimation.
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Advances in Sensitivity Analysis
Reliability Engineering & System Safety, 2012Co-Authors: Emanuele Borgonovo, Stefano TarantolaAbstract:Abstract This editorial presents the content of the special issue Advances in Sensitivity Analysis that follows the Sixth International Conference on Sensitivity Analysis of Model Output (SAMO 2010). The special issue highlights the state of the art in a field which is rapidly growing and whose importance is more and more recognized by the scientific community at large, as testified by the wide range of theoretical and applied problems addressed in this special issue.
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Sensitivity Analysis for Hydraulic Models
Journal of Hydraulic Engineering, 2009Co-Authors: Jim W. Hall, Stefano Tarantola, Shawn A. Boyce, Yueling Wang, Richard Dawson, Andrea SaltelliAbstract:Sensitivity Analysis is well recognized as being an important aspect of the responsible use of hydraulic models. This paper reviews a range of methods for Sensitivity Analysis. Two applications, one to a simple pipe bend example and the second to an advanced Shallow Water Equation solver, illustrate the deficiencies of standardized regression coefficients in the context of functionally nonlinear models. Derivatives and other local methods of Sensitivity Analysis are shown to give an incomplete picture of model response over the range of variability in the model inputs. The use of global variance-based Sensitivity Analysis is shown to be more general in its applicability and in its capacity to reflect nonlinear processes and the effects of interactions among variables.
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Sensitivity Analysis of spatial models
International Journal of Geographical Information Science, 2009Co-Authors: Linda Lilburne, Stefano TarantolaAbstract:Sensitivity Analysis is the study of how uncertainty in model predictions is determined by uncertainty in model inputs. A global Sensitivity Analysis considers the potential effects from the simultaneous variation of model inputs over their finite range of uncertainty. A number of techniques are available to carry out global Sensitivity Analysis from a set of Monte Carlo simulations; some techniques are more efficient than others, depending on the strategy used to sample the uncertainty of model inputs and on the formulae employed for estimating Sensitivity measures. The most common approaches are summarised in this paper by focusing on the limitations of each in the context of a Sensitivity Analysis of a spatial model. A novel approach for undertaking a spatial Sensitivity Analysis (based on the method of Sobol' and its related improvements) is proposed and tested. This method makes no assumptions about the model and enables the Analysis of spatially distributed, uncertain inputs. The proposed approach is illustrated with a simple test model and a groundwater contaminant model.
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Global Sensitivity Analysis. The Primer - Introduction to Sensitivity Analysis
Global Sensitivity Analysis. The Primer, 2008Co-Authors: Andrea Saltelli, T Andres, F Campolongo, D Gatelli, Michaela Saisana, Matt Ratto, Jessica Cariboni, Stefano TarantolaAbstract:Sensitivity Analysis provides users of mathematical and simulation models with tools to appreciate the dependency of the model output from model input and to investigate how important is each model input in determining its output. All application areas are concerned, from theoretical physics to engineering and socio-economics. This introductory paper provides the Sensitivity Analysis aims and objectives in order to explain the composition of the overall “Sensitivity Analysis” chapter of the Springer Handbook. It also describes the basic principles of Sensitivity Analysis, some classification grids to understand the application B. Iooss ( ) Industrial Risk Management Department, EDF RD biooss@yahoo.fr A. Saltelli Centre for the Study of the Sciences and the Humanities (SVT), University of Bergen (UIB), Bergen, Norway Institut de Ciencia i Tecnologia Ambientals (ICTA), Universitat Autonoma de Barcelona (UAB), Barcelona, Spain e-mail: andrea.saltelli@svt.uib.no; andrea.saltelli@jrc.ec.europa.eu © Springer International Publishing Switzerland 2015 R. Ghanem et al. (eds.), Handbook of Uncertainty Quantification, DOI 10.1007/978-3-319-11259-6_31-1 1 2 B. Iooss and A. Saltelli ranges of each method, a useful software package, and the notations used in the chapter papers. This section also offers a succinct description of Sensitivity auditing, a new discipline that tests the entire inferential chain including model development, implicit assumptions, and normative issues and which is recommended when the inference provided by the model needs to feed into a regulatory or policy process. For the “Sensitivity Analysis” chapter, in addition to this introduction, eight papers have been written by around twenty practitioners from different fields of application. They cover the most widely used methods for this subject: the deterministic methods as the local Sensitivity Analysis, the experimental design strategies, the sampling-based and variance-based methods developed from the 1980s, and the new importance measures and metamodelbased techniques established and studied since the 2000s. In each paper, toy examples or industrial applications illustrate their relevance and usefulness.
Tyler J Vanderweele - One of the best experts on this subject based on the ideXlab platform.
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Sensitivity Analysis without assumptions
Epidemiology, 2016Co-Authors: Peng Ding, Tyler J VanderweeleAbstract:Unmeasured confounding may undermine the validity of causal inference with observational studies. Sensitivity Analysis provides an attractive way to partially circumvent this issue by assessing the potential influence of unmeasured confounding on causal conclusions. However, previous Sensitivity ana
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Sensitivity Analysis without assumptions
arXiv: Statistics Theory, 2015Co-Authors: Peng Ding, Tyler J VanderweeleAbstract:Unmeasured confounding may undermine the validity of causal inference with observational studies. Sensitivity Analysis provides an attractive way to partially circumvent this issue by assessing the potential influence of unmeasured confounding on the causal conclusions. However, previous Sensitivity Analysis approaches often make strong and untestable assumptions such as having a confounder that is binary, or having no interaction between the effects of the exposure and the confounder on the outcome, or having only one confounder. Without imposing any assumptions on the confounder or confounders, we derive a bounding factor and a sharp inequality such that the Sensitivity Analysis parameters must satisfy the inequality if an unmeasured confounder is to explain away the observed effect estimate or reduce it to a particular level. Our approach is easy to implement and involves only two Sensitivity parameters. Surprisingly, our bounding factor, which makes no simplifying assumptions, is no more conservative than a number of previous Sensitivity Analysis techniques that do make assumptions. Our new bounding factor implies not only the traditional Cornfield conditions that both the relative risk of the exposure on the confounder and that of the confounder on the outcome must satisfy, but also a high threshold that the maximum of these relative risks must satisfy. Furthermore, this new bounding factor can be viewed as a measure of the strength of confounding between the exposure and the outcome induced by a confounder.
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Interference and Sensitivity Analysis
Statistical Science, 2014Co-Authors: Tyler J Vanderweele, Eric J. Tchetgen Tchetgen, M. Elizabeth HalloranAbstract:Causal inference with interference is a rapidly growing area. The literature has begun to relax the �no-interference� assumption that the treatment received by one individual does not affect the outcomes of other individuals. In this paper we briefly review the literature on causal inference in the presence of interference when treatments have been randomized. We then consider settings in which causal effects in the presence of interference are not identified, either because randomization alone does not suffice for identification or because treatment is not randomized and there may be unmeasured confounders of the treatment�outcome relationship. We develop Sensitivity Analysis techniques for these settings. We describe several Sensitivity Analysis techniques for the infectiousness effect which, in a vaccine trial, captures the effect of the vaccine of one person on protecting a second person from infection even if the first is infected. We also develop two Sensitivity Analysis techniques for causal effects under interference in the presence of unmeasured confounding which generalize analogous techniques when interference is absent. These two techniques for unmeasured confounding are compared and contrasted.
Emanuele Borgonovo - One of the best experts on this subject based on the ideXlab platform.
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Setup of Sensitivity Analysis
Sensitivity Analysis, 2020Co-Authors: Emanuele BorgonovoAbstract:This section is devoted to the most important step in Sensitivity Analysis, the formulation of the Sensitivity question. Scientists have developed myriads of models in different disciplines, and there are myriads of Sensitivity Analysis methods waiting to be used to explore the content of those models.
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Global Sensitivity Analysis
Sensitivity Analysis, 2020Co-Authors: Emanuele BorgonovoAbstract:After completing an uncertainty Analysis , the next step is a global Sensitivity Analysis .
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Sensitivity Analysis a review of recent advances
European Journal of Operational Research, 2016Co-Authors: Emanuele Borgonovo, Elmar PlischkeAbstract:The solution of several operations research problems requires the creation of a quantitative model. Sensitivity Analysis is a crucial step in the model building and result communication process. Through Sensitivity Analysis we gain essential insights on model behavior, on its structure and on its response to changes in the model inputs. Several interrogations are possible and several Sensitivity Analysis methods have been developed, giving rise to a vast and growing literature. We present an overview of available methods, structuring them into local and global methods. For local methods, we discuss Tornado diagrams, one way Sensitivity functions, differentiation-based methods and scenario decomposition through finite change Sensitivity indices, providing a unified view of the associated Sensitivity measures. We then analyze global Sensitivity methods, first discussing screening methods such as sequential bifurcation and the Morris method. We then address variance-based, moment-independent and value of information-based Sensitivity methods. We discuss their formalization in a common rationale and present recent results that permit the estimation of global Sensitivity measures by post-processing the sample generated by a traditional Monte Carlo simulation. We then investigate in detail the methodological issues concerning the crucial step of correctly interpreting the results of a Sensitivity Analysis. A classical example is worked out to illustrate some of the approaches.
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Advances in Sensitivity Analysis
Reliability Engineering & System Safety, 2012Co-Authors: Emanuele Borgonovo, Stefano TarantolaAbstract:Abstract This editorial presents the content of the special issue Advances in Sensitivity Analysis that follows the Sixth International Conference on Sensitivity Analysis of Model Output (SAMO 2010). The special issue highlights the state of the art in a field which is rapidly growing and whose importance is more and more recognized by the scientific community at large, as testified by the wide range of theoretical and applied problems addressed in this special issue.
