Journal of the Korean Statistical Society

The Korean Statistical Society (한국통계학회)
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Bayesian Outlier Detection in Regression Model

  • Younshik Chung (Department of Statistics, Pusan National University) ;
  • Kim, Hyungsoon (Department of Statistics, Pusan National University)
  • Published : 1999.09.01
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Abstract

The problem of 'outliers', observations which look suspicious in some way, has long been one of the most concern in the statistical structure to experimenters and data analysts. We propose a model for an outlier problem and also analyze it in linear regression model using a Bayesian approach. Then we use the mean-shift model and SSVS(George and McCulloch, 1993)'s idea which is based on the data augmentation method. The advantage of proposed method is to find a subset of data which is most suspicious in the given model by the posterior probability. The MCMC method(Gibbs sampler) can be used to overcome the complicated Bayesian computation. Finally, a proposed method is applied to a simulated data and a real data.

Keywords

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