Combining statistical and expert evidence using belief functions: Application to centennial sea level estimation taking into account climate change

Abdallah, Nadia Ben; Mouhous-Voyneau, Nassima; Denux, Thierry

doi:10.1016/j.ijar.2013.03.008

Cited by 42 publications

(27 citation statements)

References 34 publications

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“…An example of such combination for quantifying the uncertainty of sea level rise due to climate change has been presented in [4,5], and further work along this line is under way.…”

Section: Discussionmentioning

confidence: 99%

Likelihood-based belief function: Justification and some extensions to low-quality data

Denux

2014

International Journal of Approximate Reasoning

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“…An example of such combination for quantifying the uncertainty of sea level rise due to climate change has been presented in [4,5], and further work along this line is under way.…”

Section: Discussionmentioning

confidence: 99%

Likelihood-based belief function: Justification and some extensions to low-quality data

Denux

2014

International Journal of Approximate Reasoning

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“…However, this does not mean there is not useful information on these more extreme changes [100]. This raises the need for other theories of uncertainties able to convey differences among different estimates while minimizing the introduction of arbitrary information in uncertainty representations [153,154]. These latter approaches are complementary to probabilistic descriptions of uncertainties: in some coastal areas, users will require an optimal response to SLR through probabilistic projections [89,155], whereas others will use sea level scenarios or projections conveying minimum or maximum SLR estimates, in order to estimate minimum adaptation needs or to explore high end scenarios 1 .…”

Section: Barriers To Providing Information On Uncertainties Of Futurementioning

confidence: 99%

Sea Level Change and Coastal Climate Services: The Way Forward

Cozannet

Nicholls

Hinkel

et al. 2017

JMSE

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For many climate change impacts such as drought and heat waves, global and national frameworks for climate services are providing ever more critical support to adaptation activities. Coastal zones are especially in need of climate services for adaptation, as they are increasingly threatened by sea level rise and its impacts, such as submergence, flooding, shoreline erosion, salinization and wetland change. In this paper, we examine how annual to multi-decadal sea level projections can be used within coastal climate services (CCS). To this end, we review the current state-of-the art of coastal climate services in the US, Australia and France, and identify lessons learned. More broadly, we also review current barriers in the development of CCS, and identify research and development efforts for overcoming barriers and facilitating their continued growth. The latter includes: (1) research in the field of sea level, coastal and adaptation science and (2) cross-cutting research in the area of user interactions, decision making, propagation of uncertainties and overall service architecture design. We suggest that standard approaches are required to translate relative sea level information into the forms required to inform the wide range of relevant decisions across coastal management, including coastal adaptation.

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“…A complete exposition in the finite case can be found in Shafer's book [20]. The reader is referred to [5] for a quick introduction on those aspects of this theory needed for statistical inference. In this section, the definition of a belief function from the likelihood function and the general prediction method introduced in [16] will be recalled in Sects.…”

Section: Inference and Prediction Using Belief Functionsmentioning

confidence: 99%

Estimation and Prediction Using Belief Functions: Application to Stochastic Frontier Analysis

Kanjanatarakul

Kaewsompong

Sriboonchitta

et al. 2014

Econometrics of Risk

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We outline an approach to statistical inference based on belief functions. For estimation, a consonant belief functions is constructed from the likelihood function. For prediction, the method is based on an equation linking the unobserved random quantity to be predicted, to the parameter and some underlying auxiliary variable with known distribution. The approach allows us to compute a predictive belief function that reflects both estimation and random uncertainties. The method is invariant to one-to-one transformations of the parameter and compatible with Bayesian inference, in the sense that it yields the same results when provided with the same information. It does not, however, require the user to provide prior probability distributions. The method is applied to stochastic frontier analysis with cross-sectional data. We demonstrate how predictive belief functions on inefficiencies can be constructed for this problem and used to assess the plausibility of various assertions.

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Combining statistical and expert evidence using belief functions: Application to centennial sea level estimation taking into account climate change

Cited by 42 publications

References 34 publications

Likelihood-based belief function: Justification and some extensions to low-quality data

Likelihood-based belief function: Justification and some extensions to low-quality data

Sea Level Change and Coastal Climate Services: The Way Forward

Estimation and Prediction Using Belief Functions: Application to Stochastic Frontier Analysis

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