A Nonparametric Bootstrap Comparison of Variances of Robust Regression Estimators.

Result code in IS VaVaI

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Result on the web

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DOI - Digital Object Identifier

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Result language

angličtina

Original language name

A Nonparametric Bootstrap Comparison of Variances of Robust Regression Estimators.

Original language description

While various robust regression estimators are available for the standard linear regression model, performance comparisons of individual robust estimators over real or simulated datasets seem to be still lacking. In general, a reliable robust estimator of regression parameters should be consistent and at the same time should have a relatively small variability, i.e. the variances of individual regression parameters should be small. The aim of this paper is to compare the variability of S-estimators, MM-estimators, least trimmed squares, and least weighted squares estimators. While they all are consistent under general assumptions, the asymptotic covariance matrix of the least weighted squares remains infeasible, because the only available formula for its computation depends on the unknown random errors. Thus, we take resort to a nonparametric bootstrap comparison of variability of different robust regression estimators. It turns out that the best results are obtained either with MM-estimators, or with the least weighted squares with suitable weights. The latter estimator is especially recommendable for small sample sizes.

Czech name

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Czech description

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Type

D - Article in proceedings

CEP classification

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OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

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

2019

Confidentiality

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

Article name in the collection

Conference Proceedings. 37th International Conference on Mathematical Methods in Economics 2019

ISBN

978-80-7394-760-6

ISSN

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e-ISSN

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Number of pages

6

Pages from-to

168-173

Publisher name

University of South Bohemia in České Budějovice, Faculty of Economics

Place of publication

České Budějovice

Event location

České Budějovice

Event date

Sep 11, 2019

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

000507570400027