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METRON

, Volume 77, Issue 1, pp 1–18 | Cite as

Statistical detection and classification of background risks affecting inputs and outputs

  • Nadezhda Gribkova
  • Ričardas ZitikisEmail author
Article
  • 33 Downloads

Abstract

Systems are exposed to a variety of risks, including those known as background or systematic risks. Therefore, advanced economic, financial, and engineering models incorporate such risks, thus inevitably making the models more challenging to explore. A number of natural questions arise. First and foremost, is the given system affected by any of such risks? If so, then is the system affected by the risks at the input or output stage, or at both stages? In the present paper we construct an algorithm that answers such questions. Even though the algorithm is based on intricate probabilistic considerations, its practical implementation is easy.

Keywords

Input Output Background risk Gini index Statistical model 

Notes

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Copyright information

© Sapienza Università di Roma 2019

Authors and Affiliations

  1. 1.Faculty of Mathematics and MechanicsSaint Petersburg State UniversitySaint PetersburgRussia
  2. 2.School of Mathematical and Statistical SciencesWestern UniversityLondonCanada

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