Abstract
We consider the problem of regression estimation within a Bayesian framework for models linear in the parameters and where the target variables are contaminated by ‘outliers’. We introduce an explicit distribution to explain outlying observations, and utilise a variational approximation to realise a practical inference strategy.
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Faul, A.C., Tipping, M.E. (2001). A Variational Approach to Robust Regression. In: Dorffner, G., Bischof, H., Hornik, K. (eds) Artificial Neural Networks — ICANN 2001. ICANN 2001. Lecture Notes in Computer Science, vol 2130. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44668-0_14
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DOI: https://doi.org/10.1007/3-540-44668-0_14
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