A resisting gross errors capability study of robust estimation of unary linear regression method

Robust estimation methods are often used to eliminate or weaken the influences of gross errors on parameter estimation. However, different robust estimation methods may have different capabilities in eliminating or weakening gross errors. Taking unary linear regression as example, simulation experim...

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Bibliographic Details
Published inCommunications in statistics. Simulation and computation Vol. 46; no. 2; pp. 815 - 822
Main Authors Jia, Chao, Ge, Yonghui
Format Journal Article
LanguageEnglish
Published Philadelphia Taylor & Francis 07.02.2017
Taylor & Francis Ltd
Subjects
Comparison of methods
Computation
Computer simulation
Derivatives
Economic models
Error analysis
Errors
Estimating techniques
Methods
Parameter estimation
Regression
Regression analysis
Robust estimation
Robustness
Robustness (mathematics)
Unary linear regression
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Summary:Robust estimation methods are often used to eliminate or weaken the influences of gross errors on parameter estimation. However, different robust estimation methods may have different capabilities in eliminating or weakening gross errors. Taking unary linear regression as example, simulation experiments are used to compare 14 frequently used robust estimation methods. The current article summarizes the common characteristics and rules of the robust estimation methods. Finally, we confirm several relatively more efficient methods for unary linear regression.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
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ISSN:0361-0918
1532-4141
DOI:10.1080/03610918.2014.963608