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

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Bayesian inference using least median of squares and least trimmed squares in models with independent or correlated errors and outliers. / Tsionas, Mike.
In: Communications in Statistics - Theory and Methods, 16.07.2023.

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Harvard

Tsionas, M 2023, 'Bayesian inference using least median of squares and least trimmed squares in models with independent or correlated errors and outliers', Communications in Statistics - Theory and Methods. https://doi.org/10.1080/03610926.2023.2232905

APA

Tsionas, M. (2023). Bayesian inference using least median of squares and least trimmed squares in models with independent or correlated errors and outliers. Communications in Statistics - Theory and Methods. Advance online publication. https://doi.org/10.1080/03610926.2023.2232905

Vancouver

Tsionas M. Bayesian inference using least median of squares and least trimmed squares in models with independent or correlated errors and outliers. Communications in Statistics - Theory and Methods. 2023 Jul 16. Epub 2023 Jul 16. doi: 10.1080/03610926.2023.2232905

Author

Tsionas, Mike. / Bayesian inference using least median of squares and least trimmed squares in models with independent or correlated errors and outliers. In: Communications in Statistics - Theory and Methods. 2023.

Bibtex

@article{e361627678a841589316d3456497c028,
title = "Bayesian inference using least median of squares and least trimmed squares in models with independent or correlated errors and outliers",
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.",
keywords = "Bayesian inference, least Median of squares, least trimmed squares, stochastic volatility, ARMA models, outliers",
author = "Mike Tsionas",
year = "2023",
month = jul,
day = "16",
doi = "10.1080/03610926.2023.2232905",
language = "English",
journal = "Communications in Statistics - Theory and Methods",
issn = "0361-0926",
publisher = "Taylor and Francis Ltd.",

}

RIS

TY - JOUR

T1 - Bayesian inference using least median of squares and least trimmed squares in models with independent or correlated errors and outliers

AU - Tsionas, Mike

PY - 2023/7/16

Y1 - 2023/7/16

N2 - 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.

AB - 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.

KW - Bayesian inference

KW - least Median of squares

KW - least trimmed squares

KW - stochastic volatility

KW - ARMA models

KW - outliers

U2 - 10.1080/03610926.2023.2232905

DO - 10.1080/03610926.2023.2232905

M3 - Journal article

JO - Communications in Statistics - Theory and Methods

JF - Communications in Statistics - Theory and Methods

SN - 0361-0926

ER -