Data-based prediction under uncertainty: CDF-quantile distributions and info-gap robustness

DOI10.1016/j.jmp.2018.08.006zbMath1416.62538OpenAlexW2893537530WikidataQ129245808 ScholiaQ129245808MaRDI QIDQ1736011

Yakov Ben-Haim, Michael Smithson

Publication date: 29 March 2019

Published in: Journal of Mathematical Psychology (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.jmp.2018.08.006

zbMATH Keywords

robustnessnon-probabilistic uncertaintyprobabilistic predictiondata-based modelingCDF-quantile distributionsinfo-gap theory

Mathematics Subject Classification ID

Inference from stochastic processes and prediction (62M20) Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Nonparametric robustness (62G35)

Uses Software

• smdata
• cdfquantreg

Cites Work

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• Partition priming in judgments of imprecise probabilities
• Robustness analysis of trusses with separable load and structural uncertainties
• Generalized Linear Models for Categorical and Continuous Limited Dependent Variables
• Robustness of model-based fault diagnosis: Decisions with information-gap models of uncertainty
• A Robust Journey in the New Millennium
• CDF‐quantile distributions for modelling random variables on the unit interval
• Regression analysis of variates observed on (0, 1): percentages, proportions and fractions
• Set-models of information-gap uncertainty: Axioms and an inference scheme
• Robust rationality and decisions under severe uncertainty

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