Bayesian inference using least median of squares and least trimmed squares in models with independent or correlated errors and outliers

Mike Tsionas

Communications in Statistics - Theory and Methods, 2024, vol. 53, issue 16, 5761-5772

Abstract: We provide Bayesian inference in the context of Least Median of Squares and Least Trimmed Squares, two well-known techniques that are highly robust to outliers. We apply the new Bayesian techniques to linear models whose errors are independent or AR and ARMA. Model comparison is performed using posterior model probabilities, and the new techniques are examined using Monte Carlo experiments as well as an application to four portfolios of asset returns.

Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:taf:lstaxx:v:53:y:2024:i:16:p:5761-5772

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DOI: 10.1080/03610926.2023.2232905

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