Numerical enhancements for robust Rényi decomposable minimum distance estimators

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21340%2F18%3A00323351" target="_blank" >RIV/68407700:21340/18:00323351 - isvavai.cz</a>

Result on the web

<a href="http://dx.doi.org/10.1088/1742-6596/1141/1/012037" target="_blank" >http://dx.doi.org/10.1088/1742-6596/1141/1/012037</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1088/1742-6596/1141/1/012037" target="_blank" >10.1088/1742-6596/1141/1/012037</a>

Result language

angličtina

Original language name

Numerical enhancements for robust Rényi decomposable minimum distance estimators

Original language description

Different numerical aspects of Réniy pseudo-distance estimators are studied. These estimators are based on the minimization of information-theoretic divergences between empirical and hypothetical probability distributions. They are not classical distances, because the symmetry or triangle inequality does not hold. Robust properties of the minimum Rényi pseudodistance estimators are required by various applications in mathematical modeling, physics, or material science. Therefore we model the distribution of contaminated data as a mixture of the true distributions P and error distribution Q under different contamination level epsilon. We focus on the estimators for relatively small data samples or very sparse and scattered data with high variance, which appears mostly in high energy physics (signal and sparse background). In this case, the strict minimization leads to delta functions and it is impossible to obtain satisfactory numerical results. A way of adjusting the Rényi minimum distance estimators to these conditions is proposed. This so called 'blurring' is created as a convolution of Rényi distance with averaging Gaussian mask. Simultaneously, the effect of the input parameter alpha to the robustness is presented based on Monte-Carlo simulations for Gaussian model. Thus the Rényi distance is ready to be used in divergence decision trees for the signal versus background separations, e.g. in high energy physics NOvA or DUNE experiments at Fermilab.

Czech name

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Czech description

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Type

J_ost - Miscellaneous article in a specialist periodical

CEP classification

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OECD FORD branch

10103 - Statistics and probability

Project

<a href="/en/project/LTT18001" target="_blank" >LTT18001: Collaboration on experiments in Fermi National Accelerator Laboratory, USA</a><br>

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach

Publication year

2018

Confidentiality

C - Předmět řešení projektu podléhá obchodnímu tajemství (§ 504 Občanského zákoníku), ale název projektu, cíle projektu a u ukončeného nebo zastaveného projektu zhodnocení výsledku řešení projektu (údaje P03, P04, P15, P19, P29, PN8) dodané do CEP, jsou upraveny tak, aby byly zveřejnitelné.

Name of the periodical

Journal of Physics: Conference Series

ISSN

1742-6588

e-ISSN

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Volume of the periodical

1141

Issue of the periodical within the volume

1

Country of publishing house

GB - UNITED KINGDOM

Number of pages

5

Pages from-to

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UT code for WoS article

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EID of the result in the Scopus database

2-s2.0-85059403547