Robust estimation strategy for handling outliers

Classical estimators fail to be efficient in practical scenarios when data is riddled with extreme values known as outliers. Robust estimation strategies are insensitive to outliers and may be used in such cases. The current work is focused on developing a novel robust estimation strategy using Hube...

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Bibliographic Details
Published inCommunications in statistics. Theory and methods Vol. 53; no. 15; pp. 5311 - 5330
Main Authors Singh, G. N., Bhattacharyya, D., Bandyopadhyay, A.
Format Journal Article
LanguageEnglish
Published Philadelphia Taylor & Francis 02.08.2024
Taylor & Francis Ltd
Subjects
Extreme values
Mahalanobis distance
outliers
parameter estimation
robust estimation
robust regression
Robustness
Survey sampling
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Summary:Classical estimators fail to be efficient in practical scenarios when data is riddled with extreme values known as outliers. Robust estimation strategies are insensitive to outliers and may be used in such cases. The current work is focused on developing a novel robust estimation strategy using Huber M-estimation. A new chain-product type estimator for population mean has been suggested utilizing data on two auxiliary variables. A numerical comparison has been carried out between the proposed robust estimator and the corresponding classical estimator using real and simulated data containing outliers. Recommendations have been made for its practical use based on the encouraging results.
ISSN:0361-0926
1532-415X
DOI:10.1080/03610926.2023.2218567