Communications for Statistical Applications and Methods

The Korean Statistical Society (한국통계학회)
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A Bayesian Approach to Detecting Outliers Using Variance-Inflation Model

  • Lee, Sangjeen (School of Computer and Information, Ulsan College, Ulsan, 682-090,Korea) ;
  • Chung, Younshik (Department of Statistics, Pusan National University, Pusan, 609-735)
  • Published : 2001.12.01
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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 outliers problem and also analyze it in linear regression model using a Bayesian approach with the variance-inflation model. We will use Geweke's(1996) ideas which is based on the data augmentation method for detecting outliers in linear regression model. The advantage of the proposed method is to find a subset of data which is most suspicious in the given model by the posterior probability The sampling based approach can be used to allow the complicated Bayesian computation. Finally, our proposed methodology is applied to a simulated and a real data.

Keywords

References

  1. Convergence Diagnosis and Output Analysis Spftware for Gibbs Sampler, (Version 0.3) Best,N.C.;Cowles,M.K.;Vines,S.K.
  2. Biometrika v.55 A Bayesian Approach to Some Outlier Problems Box,G.E.P.;Tiao,G.C.
  3. Bayesian Inference in Statistical Analysis Box,G.E.P.;Tiao,G.C.
  4. Biometrika v.75 A Bayesian Approach to Outlier Detection and Residual Analysis Chaloner,K;Brant,R.
  5. American Statistician v.49 Understanding the Metropolis-Hasting algorithm Chib,S.;Greenberg,E.
  6. Journal of Korean Statistical Society v.28 Bayesian Outlier Detection in Regression Model Chung,Y.;Kim,H.
  7. in Bayesian Statistics 2 On the Predicting of Observables: A Selective Update Geisser,S.;Bernardo,J.M.(ed.);DeGroot,M.H.(ed.);Lindley,D.V(ed.);Smith,A.F.M.(ed.)
  8. In Bayesian Statistics 4 Geweke,J.J.M.Bernared(ed.);J.O.Berger(ed.);A.P.Dawid(ed.);A.F.M.Smith(ed.)
  9. Bayesian Statistics 5 Variable selection and model comparison in regression Geweke,J.;Bernardo,J.M.(ed.);Berger,J.O.(ed.);Dawid,A.P.(ed.);Smith,A.F.M.(ed.)
  10. Technometrics v.15 no.4 Care and Handling of Univariate or Multivariate Outliers in Detecting Spuriosity- A Bayesian Approach Guttman,I.
  11. Technometrics v.20 no.2 Care and Handling of Univariate Outliers in the General Linear Model to Detect Spuriosity - A Bayesian Approach Guttman,I.;Dutter,R.;Freeman,P.R.
  12. Statistical Sinica v.3 A Bayesian Look at Diagnostics in the Univariate Linear Model Guttman,I.;Pena,D.
  13. in Bayesian Statistics 2 Outliers and Influential Observations in Linear Models Pettit,L.I.;Smith,A.F.M.;Bernardo,J.M.(ed.);DeGroot,M.H.;Lindley,D.V.;Smith,A.F.M.(ed.)
  14. Biometrika v.77 no.3 Identification and Accommodation of Outliers in General Hierarchical Models Sharples,L.D.
  15. Journal of the American statistical Association v.82 The Calulation of Posterior Distributions by Data Augmentation (with Discussion) Tanner,M.;Wong,W.