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Bayesian unmasking in linear models

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  • Justel, Ana

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.

Suggested Citation

  • Justel, Ana, 1996. "Bayesian unmasking in linear models," DES - Working Papers. Statistics and Econometrics. WS 10458, Universidad Carlos III de Madrid. Departamento de Estadística.
    • Handle: RePEc:cte:wsrepe:10458
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    1. Justel, Ana & Pena, Daniel, 2001. "Bayesian unmasking in linear models," Computational Statistics & Data Analysis, Elsevier, vol. 36(1), pages 69-84, March.
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    Cited by:

    1. Justel, Ana & Pena, Daniel, 2001. "Bayesian unmasking in linear models," Computational Statistics & Data Analysis, Elsevier, vol. 36(1), pages 69-84, March.
    2. Justel, A., 1998. "Heterogeneity and model uncertainty in bayesian regression models," DES - Working Papers. Statistics and Econometrics. WS 6260, Universidad Carlos III de Madrid. Departamento de Estadística.
    3. Mohr, Donna L., 2007. "Bayesian identification of clustered outliers in multiple regression," Computational Statistics & Data Analysis, Elsevier, vol. 51(8), pages 3955-3967, May.

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    1. Mohr, Donna L., 2007. "Bayesian identification of clustered outliers in multiple regression," Computational Statistics & Data Analysis, Elsevier, vol. 51(8), pages 3955-3967, May.
    2. Justel, A., 1998. "Heterogeneity and model uncertainty in bayesian regression models," DES - Working Papers. Statistics and Econometrics. WS 6260, Universidad Carlos III de Madrid. Departamento de Estadística.

    More about this item

    Keywords

    Multiple outliers;

    Statistics

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