Characterization of Precipitation through Copulas and Expert Judgement for Risk Assessment of Infrastructure

Paprotny, Dominik; Morales-Nápoles, Oswaldo; Worm, D; Abspoel, L; Courage, W.M.G.

doi:10.1061/ajrua6.0000914

Cited by 13 publications

(25 citation statements)

References 32 publications

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“…These fields focus on decision makers and their ability to make predictions from data that cannot easily be collected and fed to a statistical model. Other areas of active research in expert opinion are quality assurance (Kläs et al, 2010), politics (Cai, Lin, Han, Liu, & Zhang, 2016; Graefe, 2015, 2018; Graefe, Armstrong, Jones Jr, & Cuzán, 2014a, 2014b; Hanea, McBride, Burgman, & Wintle, 2018; Satopää, Jensen, Mellers, Tetlock, & Ungar, 2014; Wang & Zhang, 2018), economics (Huang et al, 2016; Mak et al, 1996; Shin et al, 2013), engineering (Brito et al, 2012; Brito & Griffiths, 2016; Craig et al, 2001; Hathout, Vuillet, Peyras, Carvajal, & Diab, 2016; Jin, Lu, & Gan, 2007; Neves & Frangopol, 2008; Ren‐jun & Xian‐zhong, 2002; Tartakovsky, 2007; Wang, Du, & Cao, 2008; Zio, 1996), sports (Gu, Saaty, & Whitaker, 2016), sociology (Adams, White, & Ceylan, 2009; Cabello et al, 2012), meteorological (Abramson, Brown, Edwards, Murphy, & Winkler, 1996), ecological (Borsuk, 2004; Cooke et al, 2014; Failing et al, 2004; Johnson et al, 2018), environmental science (Li, Liu, & Yang, 2012; Mantyka‐Pringle, Martin, Moffatt, Linke, & Rhodes, 2014; Morales‐Nápoles et al, 2017; Zio & Apostolakis, 1997), and public health (Alho, 1992; Evans et al, 1994; Jana, Pramanik, Sahoo, & Mukherjee, 2019; Kurowicka, Bucura, Cooke, & Havelaar, 2010). The diversity and breadth of applications underscore the importance of expert opinion in a wide variety of disciplines.…”

Section: Introductionmentioning

confidence: 99%

“…Cooke's model also assigned experts a weight of 0 for poor predictive performance, and if an expert's performance was under some user‐set threshold they were excluded from the opinion pool. Cooke's model garnered much attention and has influenced numerous applications of combining expert opinion for forecasting (Bolger & Houlding, 2017; Brito et al, 2012; Clemen, 2008; Clemen & Winkler, 2007; Cooke, 2014, 2015; Cooke et al, 2014; Hanea et al, 2018; Hathout et al, 2016; Hora & Kardeş, 2015; Morales‐Nápoles et al, 2017; Sarin, 2013; Zio, 1996).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Aggregating predictions from experts: A review of statistical methods, experiments, and applications

McAndrew

Wattanachit

Gibson

et al. 2020

WIREs Computational Stats

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Forecasts support decision making in a variety of applications. Statistical models can produce accurate forecasts given abundant training data, but when data is sparse or rapidly changing, statistical models may not be able to make accurate predictions. Expert judgmental forecasts—models that combine expert‐generated predictions into a single forecast—can make predictions when training data is limited by relying on human intuition. Researchers have proposed a wide array of algorithms to combine expert predictions into a single forecast, but there is no consensus on an optimal aggregation model. This review surveyed recent literature on aggregating expert‐elicited predictions. We gathered common terminology, aggregation methods, and forecasting performance metrics, and offer guidance to strengthen future work that is growing at an accelerated pace. This article is categorized under: Statistical Learning and Exploratory Methods of the Data Sciences > Clustering and Classification Statistical Learning and Exploratory Methods of the Data Sciences > Exploratory Data Analysis Statistical Learning and Exploratory Methods of the Data Sciences > Modeling Methods Statistical and Graphical Methods of Data Analysis > Multivariate Analysis

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Aggregating predictions from experts: A review of statistical methods, experiments, and applications

McAndrew

Wattanachit

Gibson

et al. 2020

WIREs Computational Stats

View full text Add to dashboard Cite

show abstract

“…The bivariate dependency is modelled here through copulas, which are, loosely, joint distributions on the unit hypercube with uniform [0, 1] margins. They are applicable to any continuous joint distribution (Morales-Nápoles et al 2017 ). Firstly, random variables , and are derived from the nine indicators involved, using a dataset combining the data for the benchmark and developing countries, the latter without interpolations.…”

Section: Methodsmentioning

confidence: 99%

Convergence Between Developed and Developing Countries: A Centennial Perspective

Paprotny

2020

Soc Indic Res

Self Cite

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Are countries at a low level of socioeconomic development catching up with developed countries over time or rather falling further behind? Existing work on the subject is not conclusive, partially due to methodological differences. The aim of the paper is to carry out a broader analysis with longer time series and a more diverse set of indicators. The study divides countries of the world into 21 developed "benchmark" countries and 156 developing countries. The distance between the benchmark and developing countries is measured using the "time lags" method, applied here to nine indicators covering topics such as the economy, health, education and the environment. The study further utilizes a probabilistic approach to extrapolate missing historical data for developing countries, so that the analysis can cover a full century starting in 1920 and ending with short-term projections to year 2020. The study finds that a majority of developing countries, and the population-weighted developing world as a whole, has reduced its lag in most indicators between 1920 and 2020. Progress was unevenly distributed, with East Asian and European countries converging the most with the benchmark, while most African countries have diverged along with some American ones. Catch-up in education attainment and life expectancy has been more successful than in infant survival rate, GDP per capita or technology adoption. The findings are put in context of United Nations' Sustainable Development Goals, showing how the time lag method could improve setting targets for some of the goals. Further, time lags are used to analyze the current demographic, economic and political situation of developing countries, identifying opportunities and risks for future catch-up with developed countries.

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“…Rather, different kinds of tail dependencies that potentially induce extreme impact scenarios are prevalent. For these, we often assess and model upper and lower tail dependence exclusively (similarly to testing the goodness of fit for asymmetric, Archimedean copulas to historical data when available) given that, e.g., joint large losses are typically not observed together with joint large gains (Kousky & Cooke, ; Morales Nápoles, Paprotny, Worm, Abspoel‐Bukman, & Courage, ).…”

Section: An Illustrative Case Study: Assessing Spatial Dependence Of mentioning

confidence: 99%

Sequential Refined Partitioning for Probabilistic Dependence Assessment

Werner

Bedford²,

Quigley³

2018

Risk Analysis

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Modeling dependence probabilistically is crucial for many applications in risk assessment and decision making under uncertainty. Neglecting dependence between multivariate uncertainties can distort model output and prevent a proper understanding of the overall risk. Whenever relevant data for quantifying and modeling dependence between uncertain variables are lacking, expert judgment might be sought to assess a joint distribution. Key challenges for the use of expert judgment for dependence modeling are over- and underspecification. An expert can provide assessments that are infeasible, i.e., not consistent with any probability distribution (overspecification), and on the other hand, without making very restrictive parametric assumptions an expert cannot fully define a probability distribution (underspecification). The sequential refined partitioning method addresses over- and underspecification while allowing for flexibility about which part of a joint distribution is assessed and its level of detail. Potential overspecification is avoided by ensuring low cognitive complexity for experts through eliciting single conditioning sets and by offering feasible assessment ranges. The feasible range of any (sequential) assessment can be derived by solving a linear programming problem. Underspecification is addressed by modeling the density of directly and indirectly assessed distribution parts as minimally informative given their constraints. Hence, our method allows for modeling the whole distribution feasibly and in accordance with experts' information. A nonparametric way of assessing and modeling dependence flexibly in such detail has not been presented in the expert judgment literature for probabilistic dependence models so far. We provide an example of assessing terrorism risk in insurance underwriting.

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Characterization of Precipitation through Copulas and Expert Judgement for Risk Assessment of Infrastructure

Cited by 13 publications

References 32 publications

Aggregating predictions from experts: A review of statistical methods, experiments, and applications

Aggregating predictions from experts: A review of statistical methods, experiments, and applications

Convergence Between Developed and Developing Countries: A Centennial Perspective

Sequential Refined Partitioning for Probabilistic Dependence Assessment

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