Skip to main content
SpringerLink
Log in

Comments on: model-based clustering and classification with non-normal mixture distributions

  • Published:
Statistical Methods & Applications Aims and scope Submit manuscript

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

References

  • Fritz H, García-Escudero LA, Mayo-Iscar A (2013) A fast algorithm for robust constrained clustering. Comput Stat Data Anal 61:124–136

    Article  Google Scholar 

  • Gallegos MT, Ritter G (2009) Trimmed ML estimation of contaminated mixtures. Sankhya (Ser A) 71:164–220

    MathSciNet  MATH  Google Scholar 

  • García-Escudero LA, Gordaliza A, Matrán C, Mayo-Iscar A (2008) A general trimming approach to robust cluster analysis. Ann Stat 36:1324–1345

    Article  MATH  Google Scholar 

  • García-Escudero LA, Gordaliza A, Matrán C, Mayo-Iscar A (2011) Exploring the number of groups in robust model-based clustering. Stat Comput 21:585–599

    Article  MathSciNet  MATH  Google Scholar 

  • García-Escudero LA, Gordaliza A, Matrán C, Mayo-Iscar A (2013) A constrained robust proposal for mixture modeling avoiding spurious solutions, to appear in Adv Data Anal Classif

  • Hathaway RJ (1985) A constrained formulation of maximum likelihood estimation for normal mixture distributions. Ann Stat 13:795–800

    Article  MathSciNet  MATH  Google Scholar 

  • Ingrassia S, Rocci R (2007) Constrained monotone EM algorithms for finite mixture of multivariate Gaussians. Comput Stat Data Anal 51:5339–5351

    Article  MathSciNet  MATH  Google Scholar 

  • Lee SX, McLachlan GJ (2013) On mixtures of skew normal and skew t-distributions. Adv Data Anal Classif 7(3):241–266

    Google Scholar 

  • Neykov N, Filzmoser P, Dimova R, Neytchev P (2007) Robust fitting of mixtures using the trimmed likelihood estimator. Comput Stat Data Anal 17:299–308

    Article  MathSciNet  Google Scholar 

Download references

Acknowledgments

This research is partially supported by the Spanish Ministerio de Ciencia e Innovación, Grant MTM2011-28657-C02-01.

Author information

Authors and Affiliations

  1. IMUVA and Departamento de Estadística e Investigación Operativa, Facultad de Ciencias, Universidad de Valladolid, 47011, Valladolid, Spain

    L. A. García-Escudero, A. Gordaliza & A. Mayo-Iscar

Authors
  1. L. A. García-Escudero
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. A. Gordaliza
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. A. Mayo-Iscar
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to L. A. García-Escudero.

Rights and permissions

Reprints and permissions

About this article

Cite this article

García-Escudero, L.A., Gordaliza, A. & Mayo-Iscar, A. Comments on: model-based clustering and classification with non-normal mixture distributions. Stat Methods Appl 22, 459–461 (2013). https://doi.org/10.1007/s10260-013-0245-4

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10260-013-0245-4

Keywords

Search

Navigation