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

Research output: Contribution to Journal/MagazineJournal articlepeer-review

E-pub ahead of print
<mark>Journal publication date</mark>16/07/2023
<mark>Journal</mark>Communications in Statistics - Theory and Methods
Number of pages12
Publication StatusE-pub ahead of print
Early online date16/07/23
<mark>Original language</mark>English


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.