@article{Tindemans:2017:10.1098/rsta.2016.0299,
author = {Tindemans, SH and Strbac, G},
doi = {10.1098/rsta.2016.0299},
journal = {Philosophical Transactions A: Mathematical, Physical and Engineering Sciences},
title = {Robust estimation of risks from small samples},
url = {http://dx.doi.org/10.1098/rsta.2016.0299},
volume = {375},
year = {2017}
}

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TY  - JOUR
AB  - 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 nonparametric 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.
AU  - Tindemans,SH
AU  - Strbac,G
DO  - 10.1098/rsta.2016.0299
PY  - 2017///
SN  - 1471-2962
TI  - Robust estimation of risks from small samples
T2  - Philosophical Transactions A: Mathematical, Physical and Engineering Sciences
UR  - http://dx.doi.org/10.1098/rsta.2016.0299
UR  - http://arxiv.org/abs/1311.5052v3
VL  - 375
ER  - 

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