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Variable selection in discriminant analysis for mixed continuous-binary variables and several groups

Author

Listed:
  • Alban Mbina Mbina

    (Université des Sciences et Techniques de Masuku)

  • Guy Martial Nkiet

    (Université des Sciences et Techniques de Masuku)

  • Fulgence Eyi Obiang

    (Université des Sciences et Techniques de Masuku)

Abstract

We propose a method for variable selection in discriminant analysis with mixed continuous and binary variables. This method is based on a criterion that permits to reduce the variable selection problem to a problem of estimating suitable permutation and dimensionality. Then, estimators for these parameters are proposed and the resulting method for selecting variables is shown to be consistent. A simulation study that permits to study several properties of the proposed approach and to compare it with an existing method is given, and an example on a real data set is provided.

Suggested Citation

  • Alban Mbina Mbina & Guy Martial Nkiet & Fulgence Eyi Obiang, 2019. "Variable selection in discriminant analysis for mixed continuous-binary variables and several groups," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 13(3), pages 773-795, September.
    • Handle: RePEc:spr:advdac:v:13:y:2019:i:3:d:10.1007_s11634-018-0343-0
    DOI: 10.1007/s11634-018-0343-0
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    References listed on IDEAS

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    1. Barhen, A. & Daudin, J. J., 1995. "Generalization of the Mahalanobis Distance in the Mixed Case," Journal of Multivariate Analysis, Elsevier, vol. 53(2), pages 332-342, May.
    2. Nkiet, Guy Martial, 2012. "Direct variable selection for discrimination among several groups," Journal of Multivariate Analysis, Elsevier, vol. 105(1), pages 151-163.
    3. Daudin, J. J. & Bar-Hen, A., 1999. "Selection in discriminant analysis with continuous and discrete variables," Computational Statistics & Data Analysis, Elsevier, vol. 32(2), pages 161-175, December.
    4. W. J. Krzanowski, 1983. "Stepwise Location Model Choice in Mixed‐Variable Discrimination," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 32(3), pages 260-266, November.
    5. Fujikoshi, Yasunori, 1985. "Selection of variables in two-group discriminant analysis by error rate and Akaike's information criteria," Journal of Multivariate Analysis, Elsevier, vol. 17(1), pages 27-37, August.
    6. W. Krzanowski, 1984. "On the null distribution of distance between two groups, using mixed continuous and categorical variables," Journal of Classification, Springer;The Classification Society, vol. 1(1), pages 243-253, December.
    7. A. R. de Leon & A. Soo & T. Williamson, 2011. "Classification with discrete and continuous variables via general mixed-data models," Journal of Applied Statistics, Taylor & Francis Journals, vol. 38(5), pages 1021-1032, February.
    8. Edward J. Bedrick & Jodi Lapidus & Joseph F. Powell, 2000. "Estimating the Mahalanobis Distance from Mixed Continuous and Discrete Data," Biometrics, The International Biometric Society, vol. 56(2), pages 394-401, June.
    9. de Leon, A. R. & Carrière, K. C., 2005. "A generalized Mahalanobis distance for mixed data," Journal of Multivariate Analysis, Elsevier, vol. 92(1), pages 174-185, January.
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    Cited by:

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