Numerical enhancements for robust Rényi decomposable minimum distance estimators

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Numerical enhancements for robust Rényi decomposable minimum distance estimators

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Numerical enhancements for robust Rényi decomposable minimum distance estimators

Popis výsledku anglicky

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.

Druh

J_ost - Ostatní články v recenzovaných periodicích

CEP obor

—

OECD FORD obor

10103 - Statistics and probability

Projekt

<a href="/cs/project/LTT18001" target="_blank" >LTT18001: Spolupráce na experimentech ve Fermiho národní laboratoři, USA</a><br>

Návaznosti

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

Rok uplatnění

2018

Kód důvěrnosti údajů

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.

Název periodika

Journal of Physics: Conference Series

ISSN

1742-6588

e-ISSN

—

Svazek periodika

1141

Číslo periodika v rámci svazku

1

Stát vydavatele periodika

GB - Spojené království Velké Británie a Severního Irska

Počet stran výsledku

5

Strana od-do

—

Kód UT WoS článku

—

EID výsledku v databázi Scopus

2-s2.0-85059403547