Publication

Confidence Evaluation for Risk Prediction

2001
Reports

Abstract

This paper describe an application of a transductive method for risk mapping which allows to compute the confidence interval of an estimation, without any assumption on data distribution, except identity and independancy of inputs. The methos reliability is compared to conditionnal Sequential Gaussian Simulation. The robustness of this reliability to poor underlying regression models is also studied. The data set used is a digital elevation model of the South-West part of Switzerland ('Valais'). Experiments to evaluate the robustness of RRCM against the iid assumption are conducted using the data set of cadmium concentration in Leman Lake sediments in 1983 ('Cd83')

Official source

https://infoscience.epfl.ch/entities/publication/fb6cf138-d173-4a85-ba0b-0f37ce245f48

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2001
Reports

Abstract

Official source

https://infoscience.epfl.ch/entities/publication/fb6cf138-d173-4a85-ba0b-0f37ce245f48

About this result

Related concepts (32)

Confidence interval

In frequentist statistics, a confidence interval (CI) is a range of estimates for an unknown parameter. A confidence interval is computed at a designated confidence level; the 95% confidence level is most common, but other levels, such as 90% or 99%, are sometimes used. The confidence level, degree of confidence or confidence coefficient represents the long-run proportion of CIs (at the given confidence level) that theoretically contain the true value of the parameter; this is tantamount to the nominal coverage probability.

Confidence distribution

In statistical inference, the concept of a confidence distribution (CD) has often been loosely referred to as a distribution function on the parameter space that can represent confidence intervals of all levels for a parameter of interest. Historically, it has typically been constructed by inverting the upper limits of lower sided confidence intervals of all levels, and it was also commonly associated with a fiducial interpretation (fiducial distribution), although it is a purely frequentist concept.

Credible interval

In Bayesian statistics, a credible interval is an interval within which an unobserved parameter value falls with a particular probability. It is an interval in the domain of a posterior probability distribution or a predictive distribution. The generalisation to multivariate problems is the credible region. Credible intervals are analogous to confidence intervals and confidence regions in frequentist statistics, although they differ on a philosophical basis: Bayesian intervals treat their bounds as fixed and the estimated parameter as a random variable, whereas frequentist confidence intervals treat their bounds as random variables and the parameter as a fixed value.

Related publications (2)

Confidence Evaluation for Risk Prediction

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