Distribution-free risk analysis

Gray, Ander ORCID: 0000-0002-1585-0900, Ferson, Scott ORCID: 0000-0002-2613-0650, Kreinovich, Vladik and Patelli, Edoardo ORCID: 0000-0002-5007-7247 (2022) Distribution-free risk analysis. International Journal of Approximate Reasoning, 146. pp. 133-156.

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Abstract

Elementary formulas for propagating information about means and variances through mathematical expressions have long been used by analysts. Yet the precise implications of such information are rarely articulated. This paper explores distribution-free techniques for risk analysis that do not require simulation, sampling or approximation of any kind. We describe best-possible bounds on risks that can be inferred given only information about the range, mean and variance of a random variable. These bounds generalise the classical Chebyshev inequality in an obvious way. We also collect in convenient tables several formulas for propagating range and moment information through calculations involving 7 binary convolutions (addition, subtraction, multiplication, division, powers, minimum, and maximum) and 9 unary transformations (scalar multiplication, scalar translation, exponentiation, natural and common logarithms, reciprocal, square, square root and absolute value) commonly encountered in risk expressions. These formulas are rigorous rather than approximate, and in most cases are either exact or mathematically best-possible. The formulas can be used effectively even when only interval estimates of the moments are available. Although most discussions of moment propagation assume stochastic independence among variables, this paper shows the assumption to be unnecessary and generalises formulas for the case when no assumptions are made about dependence, and when correlations are partially known. Along with partial means and variances, we show how interval covariances may be propagated and tracked through expressions.

Item Type:	Article
Uncontrolled Keywords:	Uncertainty propagation, Moment propagation, Distribution-free risk analysis, Probability box, Dependence, Interval arithmetic
Divisions:	Faculty of Science and Engineering > School of Engineering
Depositing User:	Symplectic Admin
Date Deposited:	08 Jun 2022 08:55
Last Modified:	18 Jan 2023 21:00
DOI:	10.1016/j.ijar.2022.04.001
Open Access URL:	https://doi.org/10.1016/j.ijar.2022.04.001
Related URLs:	Author Publisher
URI:	https://livrepository.liverpool.ac.uk/id/eprint/3156048