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

Result code in 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>
Alternative codes found
RIV/67985556:_____/20:00583584
Result on the web
<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>

Result language
angličtina
Original language name
On Robust Estimation of Error Variance in (Highly) Robust Regression
Original language description
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
Czech name
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Czech description
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Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
—
OECD FORD branch
10103 - Statistics and probability

Project
Result was created during the realization of more than one project. More information in the Projects tab.
Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year
2020
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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