On Robust Estimation of Error Variance in (Highly) Robust Regression

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985807%3A_____%2F20%3A00522581" target="_blank" >RIV/67985807:_____/20:00522581 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/67985556:_____/20:00583584

Výsledek na webu

<a href="http://hdl.handle.net/11104/0307056" target="_blank" >http://hdl.handle.net/11104/0307056</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.2478/msr-2020-0002" target="_blank" >10.2478/msr-2020-0002</a>

Jazyk výsledku

angličtina

Název v původním jazyce

On Robust Estimation of Error Variance in (Highly) Robust Regression

Popis výsledku v původním jazyce

The linear regression model requires robust estimation of parameters, if the measured data are contaminated by outlying measurements (outliers). While a number of robust estimators (i.e. resistant to outliers) have been proposed, this paper is focused on estimating the variance of the random regression errors. We particularly focus on the least weighted squares estimator, for which we review its properties and´propose new weighting schemes together with corresponding estimates for the variance of disturbances. An illustrative example revealing the idea of the estimator to down-weight individual measurements is presented. Further, two numerical simulations presented here allow to compare various estimators. They verify the theoretical results for the least weighted squares to be meaningful. MM-estimators turn out to yield the best results in the simulations in terms of both accuracy and precision. The least weighted squares (with suitable weights) remain only slightly behind in terms of the mean square error and are able to outperform the much more popular least trimmed squares estimator, especially for smaller sample sizes

Název v anglickém jazyce

On Robust Estimation of Error Variance in (Highly) Robust Regression

Popis výsledku anglicky

The linear regression model requires robust estimation of parameters, if the measured data are contaminated by outlying measurements (outliers). While a number of robust estimators (i.e. resistant to outliers) have been proposed, this paper is focused on estimating the variance of the random regression errors. We particularly focus on the least weighted squares estimator, for which we review its properties and´propose new weighting schemes together with corresponding estimates for the variance of disturbances. An illustrative example revealing the idea of the estimator to down-weight individual measurements is presented. Further, two numerical simulations presented here allow to compare various estimators. They verify the theoretical results for the least weighted squares to be meaningful. MM-estimators turn out to yield the best results in the simulations in terms of both accuracy and precision. The least weighted squares (with suitable weights) remain only slightly behind in terms of the mean square error and are able to outperform the much more popular least trimmed squares estimator, especially for smaller sample sizes

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10103 - Statistics and probability

Projekt

Výsledek vznikl pri realizaci vícero projektů. Více informací v záložce Projekty.

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2020

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Measurement Science Review

ISSN

1335-8871

e-ISSN

—

Svazek periodika

20

Číslo periodika v rámci svazku

1

Stát vydavatele periodika

SK - Slovenská republika

Počet stran výsledku

9

Strana od-do

6-14

Kód UT WoS článku

000517823000002

EID výsledku v databázi Scopus

2-s2.0-85081789945