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Abstract

The resolution of a mixture of two or more populations into its component distributions may be markedly influenced by one or a few atypical values. The maximum likelihood solution effectively assigns (part of) each observation to one or another of the components via the posterior probabilities, even though the observation may be widely discrepant from all components. This paper presents a modification of the mixture problem, in which the typicality of each observation is considered, as well as the posterior probabilities, with the contribution of atypical observations being downweighted. The extension to the multivariate case is discussed.

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References

  • Aitchison, J., Habbema, J. D. F., and Kay, J. W., 1977, A critical comparison of two methods of statistical discrimination: Appl. Stat., v. 26, no. 1, p. 15–25.

    Google Scholar 

  • Campbell, N. A., 1980, Robust procedures in multivariate analysis, I, Robust covariance estimation: Appl. Stat., v. 29, no. 3, p. 231–237.

    Google Scholar 

  • Clarke, M. R. B., 1971, Updating the sample mean and dispersion matrix: Appl. Stat., v. 20, no. 2, p. 206–209.

    Google Scholar 

  • Collins, J. R., 1976, Robust estimation of a location parameter in the presence of asymmetry: Ann. Stat., v. 4, no. 1, p. 68–85.

    Google Scholar 

  • Day, N. E., 1969, Estimating the components of a mixture of normal distributions: Biometrika, v. 56, no. 3, p. 463–474.

    Google Scholar 

  • Dempster, A. P., Laird, N. M., and Rubin, D. B., 1977, Maximum likelihood from incomplete data via the EM algorithm (with Discussion): Jour. Roy. Stat. Soc. Ser. B., v. 39, no. 1, p. 1–38.

    Google Scholar 

  • Fowlkes, E. B., 1979, Some methods for studying the mixture of two normal (lognormal) distributions: J. Amer. Stat. Assoc., v. 74, no. 367, p. 561–575.

    Google Scholar 

  • Hampel, F. R., 1973, Robust estimation: A condensed partial survey: Z. Wahr. verw. Geb., v. 27, p. 87–104.

    Google Scholar 

  • Hampel, F. R., 1974, The influence curve and its role in robust estimation: J. Amer. Stat. Assoc., v. 69, no. 346, p. 383–393.

    Google Scholar 

  • Hasselblad, V., 1966, Estimation of parameters for a mixture of normal distributions: Technometrics, v. 8, no. 3, p. 431–446.

    Google Scholar 

  • Hogg, R. V., 1977, An introduction to robust procedures: Commun. Statist.—Theor. Meth., v. A6, no. 9, p. 789–794.

    Google Scholar 

  • Holgersson, M. and Jorner, U., 1978, Decomposition of a mixture into normal components: A review: J. Bio-Med. Comp., v. 9, p. 367–392.

    Google Scholar 

  • Holland, P. W. and Welsch, R. E., 1977, Robust regression using iteratively reweighted leastsquares: Commun. Stat.—Theor. Meth., v. A6, no. 9, p. 813–827.

    Google Scholar 

  • Huber, P. J., 1964, Robust estimation for a location parameter: Ann. Math. Stat., v. 35, no. 1, p. 73–101.

    Google Scholar 

  • Sinclair, A. J., 1976, Applications of Probability Graphs in Mineral Exploration. Assoc. of Exploration Geochemists, Rexdale, Ontario, 95 pp.

    Google Scholar 

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Campbell, N.A. Mixture models and atypical values. Mathematical Geology 16, 465–477 (1984). https://doi.org/10.1007/BF01886327

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  • DOI: https://doi.org/10.1007/BF01886327

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