Risk Analysis: A Quantitative Guide

By D. Vose. 2000

1,687 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/0b1de7cf741a1573b7de8fab96e5df1a7ede3543

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Walid Klibi
2. Adel Guitouni
3. Michael Weisberg
4. Luis Garrote
5. R. Kroes
6. S. Quiroga
7. M. Moneo
8. J. Kleiner

Citations in the corpus

• A regional comparison of the effects of climate change on agricultural crops in Europe (2012, background): “In addition to the uncertainty, the results may present a probabilistic variation (risk, Hardaker et al. 2004; Vose 2000).”
• Burden of Six Healthcare-Associated Infections on European Population Health: Estimating Incidence-Based Disability-Adjusted Life Years through a Population Prevalence-Based Modelling Study (2016, methodology): “Where applicable, inputs for diseasemodel parameters and HAI incidence data included uncertainty intervals, which were incorporated in the calculations as uniform (two variables) or Project Evaluation and ReviewTechniques (PERT) (three variables) distributions [25].”
• Management of disease in wild mammals (2009, methodology): “The computational cost of such a scheme can be reduced by employing an intelligent sampling scheme such as Latin Hypercube sampling (Vose 2001).”
• Comprehensive flood risk assessment based on set pair analysis-variable fuzzy sets model and fuzzy AHP (2013, methodology): “Based on the disaster system theory and following the principle of systematic, quantitative, operability and universality (Huang 2005; Wei et al. 2007; Vose 2008), the primary flood risk assessment index system of flood diversion district was established in this paper, as shown in Fig.”
• Multilevel Fuzzy Approach to the Risk and Disaster Management (2010, methodology): “The presented quantitative methods for risk analysis in [2] are based on well-known mathematical models of expert systems, quantitative optimum calculation models, statistical hypothesis and possibility theory.”
• The design of robust value-creating supply chain networks (2010, methodology): “CIRRELT-2008-36 13 Rosenhead et al. (1972) proposed to distinguish between decision-making under certainty, risk and uncertainty, and this characterization was subsequently adopted by several authors (Kouvelis and Yu, 1997; Vose, 2000; Snyder, 2006).”
• Economies of contagion: financial crisis and pandemic (2013, background): “Epidemiological risk modelling ‘defined as the formal, quantitative estimation of the probability of specified adverse effects from defined hazards’ (Vose, 2008, quoted in Woolhouse, 2011, p. 2048) is clearly of interest to financial theorists because its findings have been successfully integrated…”
• The Value of Using Imprecise Probabilities in Engineering Design (2006, background): “By applying the expected value operator, the decision-maker weights all possible outcomes according to their likelihood of occurring, and then chooses the action that maximizes the expected utility.”
• Improving risk matrices: the advantages of logarithmically scaled axes (2012, background): “Vose (2008) encourages analysts constructing risk matrices to use logarithmic axes.”