Bayesian Unmasking in Linear Models

Ana Justel and Daniel Peña
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Ana Justel: Center for Operations Research and Econometrics (CORE), Université catholique de Louvain (UCL), Louvain la Neuve, Belgium

No 1996019, LIDAM Discussion Papers CORE from Université catholique de Louvain, Center for Operations Research and Econometrics (CORE)

Abstract: We propose a Bayesian procedure for multiple outlier detection in linear models avoiding the masking problem. Our proposal is illustrated with several examples in which our procedure outperforms other recent methods for multiple outlier detection. The posterior probabilities of each data point being an outlier are estimated by using a new adaptive Gibbs sampling method, which modifies the initial conditions of the Gibbs sampler by using the eigenstructure of the covariance matrix of the indicator variables. This procedure also overcomes the false convergence of the Gibbs sampling in problems with strong masking.

Keywords: Gibbs sampler; Linear regression; Multiple outliers; Sequential learning (search for similar items in EconPapers)
Date: 1996-05-01
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Citations: View citations in EconPapers (2)

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Related works:
Journal Article: Bayesian unmasking in linear models (2001)
Working Paper: Bayesian unmasking in linear models (1996)
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Persistent link: https://EconPapers.repec.org/RePEc:cor:louvco:1996019

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