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A User-Friendly Algorithm for Detecting the Influence of Background Risks on a Model. / Грибкова, Надежда Викторовна; Zitikis, Ričardas.

In: Risks, Vol. 6, No. 3, 100, 14.09.2018, p. 1-15.

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@article{9a04c2039dfe4e80a889df95a15665f7,
title = "A User-Friendly Algorithm for Detecting the Influence of Background Risks on a Model",
abstract = "Background, or systematic, risks are integral parts of many systems and models in insurance and finance. These risks can, for example, be economic in nature, or they can carry more technical connotations, such as errors or intrusions, which could be intentional or unintentional. A most natural question arises from the practical point of view: is the given system really affected by these risks? In this paper we offer an algorithm for answering this question, given input-output data and appropriately constructed statistics, which rely on the order statistics of inputs and the concomitants of outputs. Even though the idea is rooted in complex statistical and probabilistic considerations, the algorithm is easy to implement and use in practice, as illustrated using simulated data.",
keywords = "background risk, systematic risk, transfer function, information processing, order statistic, concomitant, background risk, systematic risk, transfer function, information processing, order statistic, concomitant, CAPITAL ALLOCATIONS, ATTACKS",
author = "Грибкова, {Надежда Викторовна} and Ri{\v c}ardas Zitikis",
year = "2018",
month = sep,
day = "14",
doi = "10.3390/risks6030100",
language = "English",
volume = "6",
pages = "1--15",
journal = "Risks",
issn = "2227-9091",
publisher = "MDPI AG",
number = "3",

}

RIS

TY - JOUR

T1 - A User-Friendly Algorithm for Detecting the Influence of Background Risks on a Model

AU - Грибкова, Надежда Викторовна

AU - Zitikis, Ričardas

PY - 2018/9/14

Y1 - 2018/9/14

N2 - Background, or systematic, risks are integral parts of many systems and models in insurance and finance. These risks can, for example, be economic in nature, or they can carry more technical connotations, such as errors or intrusions, which could be intentional or unintentional. A most natural question arises from the practical point of view: is the given system really affected by these risks? In this paper we offer an algorithm for answering this question, given input-output data and appropriately constructed statistics, which rely on the order statistics of inputs and the concomitants of outputs. Even though the idea is rooted in complex statistical and probabilistic considerations, the algorithm is easy to implement and use in practice, as illustrated using simulated data.

AB - Background, or systematic, risks are integral parts of many systems and models in insurance and finance. These risks can, for example, be economic in nature, or they can carry more technical connotations, such as errors or intrusions, which could be intentional or unintentional. A most natural question arises from the practical point of view: is the given system really affected by these risks? In this paper we offer an algorithm for answering this question, given input-output data and appropriately constructed statistics, which rely on the order statistics of inputs and the concomitants of outputs. Even though the idea is rooted in complex statistical and probabilistic considerations, the algorithm is easy to implement and use in practice, as illustrated using simulated data.

KW - background risk

KW - systematic risk

KW - transfer function

KW - information processing

KW - order statistic

KW - concomitant

KW - background risk

KW - systematic risk

KW - transfer function

KW - information processing

KW - order statistic

KW - concomitant

KW - CAPITAL ALLOCATIONS

KW - ATTACKS

U2 - 10.3390/risks6030100

DO - 10.3390/risks6030100

M3 - Article

VL - 6

SP - 1

EP - 15

JO - Risks

JF - Risks

SN - 2227-9091

IS - 3

M1 - 100

ER -

ID: 34784929