Advertisement

Robustizing Mixture Analysis Using Model Weighting

  • Michael P. Windham
Conference paper
Part of the Studies in Classification, Data Analysis, and Knowledge Organization book series (STUDIES CLASS)

Summary

This paper presents two methods for modifying a statistical estimation or model fitting procedure. The first shows how to extend the procedure to mixture distributions. The second shows how to make the procedure more robust using weighting. The two modifications are then combined to produce robust methods for mixture analysis.

Keywords

Mixture Distribution Model Family Mixture Analysis Normal Mixture Asymptotic Efficiency 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. DEMPSTER, A.P., LAIRD, N.M., and RUBIN, D.B. (1977): Maximum-likeli– hood from Incomplete Data Via the EM Algorithm. Journal of the Royal Statistical Society, Series B, 39, pp. 1–38.Google Scholar
  2. WINDHAM, M.P., (1994a) Mixture Analysis with Noisy Data. In New Approaches in Classification and Data Analysis. Proceedings of the Fourth Conference of the International Federation of Classification Societies (IFCS-93). E. Diday, Y. Lechavllier, M. Schader, P. Bertrand, B. Burtschy (eds.) Heidelberg: Springer-Verlag, 155–160.Google Scholar
  3. WINDHAM, M.P., (1994b) Robustizing Model Fitting. Journal of the Royal Statistical Society, submitted.Google Scholar
  4. WINDHAM M.P. and CUTLER A. (1992): Information Ratios for Validating Mixture Analyses. Journal of the American Statistical Association, 87, 1188–1192.CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin · Heidelberg 1996

Authors and Affiliations

  • Michael P. Windham
    • 1
  1. 1.Mathematics and StatisticsUtah State UniversityLoganUSA

Personalised recommendations