Skip to main content
Log in

An alternative approach to discriminating multivariate populations and its applications to marketing research

  • Published:
Marketing Letters Aims and scope Submit manuscript

Abstract

A new method for discriminating among multivariate populations, called the Hausdorff procedure, is introduced to the marketing literature. Rules for classification are defined and a limited simulation study is conducted. For the simulation, both the level of collinearity among the discriminating variables and the level of overlap among the populations are varied. The results indicate that this new procedure is particularly suitable when there is either a high degree of collinearity among the predictor variables or considerable overlap of the populations being investigated. The Hausdorff procedure is also applied to two sets of consumer data. In each instance, it is found to be superior to linear discriminant analysis with respect to the percentage of correct classifications.

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

Access this article

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

Instant access to the full article PDF.

Similar content being viewed by others

References

  • Barnsley, M. (1988).Fractals Everywhere. Boston: Academic Press.

    Google Scholar 

  • Bernstein, I. H. (1988).Applied Multivariate Analysis. New York: Springer Verlag.

    Google Scholar 

  • BMDP Statistical Software Manual. (1985). Berkeley: University of California Press.

  • Chatterjee, S., and Narayanan, A. (1992). “A New Approach to Discrimination and Classification Using a Hausdorff Type Distance,”Australian Journal of Statistics 34, 391–406.

    Google Scholar 

  • Chatterjee, S., and Chatterjee, S. (1983). “Estimation of Misclassification Probabilities by Bootstrap Methods,”Communications in Statistics. Simulation and Computations 12, 645–656.

    Google Scholar 

  • Cochran, W. G. (1961). “On the Performance of the Linear Discriminant Function,”Bulletin of the International Statistical Institute 39, 435–447.

    Google Scholar 

  • Cooper, N. G. (1988).From Cardinals to Chaos. Cambridge: Cambridge University Press.

    Google Scholar 

  • Crask, M. R., and W. D. Perrault. (1977). “Validation to Discriminant Analysis in Marketing Research,”Journal of Marketing Research 11, 60–64.

    Google Scholar 

  • Efron, B. (1979). “Bootstrap Methods: Another Look at the Jackknife,”Annals of Statistics 7, 1–26.

    Google Scholar 

  • Friedman, J. (1989). “Regularized Discriminant Analysis,”Journal of the American Statistical Association 84, 165–175.

    Google Scholar 

  • IMSL Inc. (1989).STAT/LIBRARY User's Manual, Houston, Texas 1015–1016.

  • Lachenbruch, P. A., and M. R. Mickey. (1968). “Estimation of Error Rates in Discriminant Analysis,”Technometrics 10, 1–11.

    Google Scholar 

  • Marco, V., D. M. Young, and D. W. Turner. (1987). “The Euclidian Distance Classifier: An Alternative to the Linear Distance Classifier,”Communications in Statistics-Simulation and Computation 16, 485–505.

    Google Scholar 

  • McLachlan, G. J. (1987). “Error Rate Estimation in Discriminant Analysis: Recent Advances.” InAdvances in Multivariate Statistical Analysis (Ed. A. K. Gupta). Dordrecht, Holland: D. Reidel Publishing Company.

    Google Scholar 

  • SAS Institute Inc. (1985).SAS User's Guide: Statistics, Version 5. Cary, NC: Author.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Rights and permissions

Reprints and permissions

About this article

Cite this article

Chatterjee, S., Narayanan, A. & Wiseman, F. An alternative approach to discriminating multivariate populations and its applications to marketing research. Marketing Letters 4, 349–360 (1993). https://doi.org/10.1007/BF00994353

Download citation

  • Issue Date:

  • DOI: https://doi.org/10.1007/BF00994353

Key words

Navigation