Robust estimation of risks from small samples

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Title: Robust estimation of risks from small samples
Authors: Tindemans, S
Strbac, G
Item Type: Journal Article
Abstract: Data-driven risk analysis involves the inference of probability distributions from measured or simulated data. In the case of a highly reliable system, such as the electricity grid, the amount of relevant data is often exceedingly limited, but the impact of estimation errors may be very large. This paper presents a robust non-parametric Bayesian method to infer possible underlying distributions. The method obtains rigorous error bounds even for small samples taken from ill-behaved distributions. The approach taken has a natural interpretation in terms of the intervals between ordered observations, where allocation of probability mass across intervals is well specified, but the location of that mass within each interval is unconstrained. This formulation gives rise to a straightforward computational resampling method: Bayesian interval sampling. In a comparison with common alternative approaches, it is shown to satisfy strict error bounds even for ill-behaved distributions.
Issue Date: 10-Jul-2017
Date of Acceptance: 5-Apr-2017
URI: http://hdl.handle.net/10044/1/46104
DOI: https://dx.doi.org/10.1098/rsta.2016.0299
ISSN: 1471-2962
Publisher: Royal Society, The
Journal / Book Title: Philosophical Transactions A: Mathematical, Physical and Engineering Sciences
Volume: 375
Copyright Statement: © 2017 The Author(s) Published by the Royal Society. All rights reserved.
Sponsor/Funder: Commission of the European Communities
Funder's Grant Number: PIEF-GA-2010-274387
Keywords: stat.ME
stat.ME
MD Multidisciplinary
General Science & Technology
Publication Status: Published
Article Number: 20160299
Appears in Collections:Faculty of Engineering
Electrical and Electronic Engineering
Centre for Environmental Policy
Faculty of Natural Sciences



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