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Classification with discrete and continuous variables via general mixed-data models

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  • A. R. de Leon
  • A. Soo
  • T. Williamson

Abstract

We study the problem of classifying an individual into one of several populations based on mixed nominal, continuous, and ordinal data. Specifically, we obtain a classification procedure as an extension to the so-called location linear discriminant function, by specifying a general mixed-data model for the joint distribution of the mixed discrete and continuous variables. We outline methods for estimating misclassification error rates. Results of simulations of the performance of proposed classification rules in various settings vis-à-vis a robust mixed-data discrimination method are reported as well. We give an example utilizing data on croup in children.

Suggested Citation

  • 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.
    • Handle: RePEc:taf:japsta:v:38:y:2011:i:5:p:1021-1032
    DOI: 10.1080/02664761003758976
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    1. Wai-Yin Poon & Sik-Yum Lee, 1987. "Maximum likelihood estimation of multivariate polyserial and polychoric correlation coefficients," Psychometrika, Springer;The Psychometric Society, vol. 52(3), pages 409-430, September.
    2. Marian Núñez & Angel Villarroya & José María Oller, 2003. "Minimum Distance Probability Discriminant Analysis for Mixed Variables," Biometrics, The International Biometric Society, vol. 59(2), pages 248-253, June.
    3. Ming Tan & Yinsheng Qu & J. Sunil Rao, 1999. "Robustness of the Latent Variable Model for Correlated Binary Data," Biometrics, The International Biometric Society, vol. 55(1), pages 258-263, March.
    4. W. Krzanowski, 1993. "The location model for mixtures of categorical and continuous variables," Journal of Classification, Springer;The Classification Society, vol. 10(1), pages 25-49, January.
    5. 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:

    1. Leila Amiri & Mojtaba Khazaei & Mojtaba Ganjali, 2017. "General location model with factor analyzer covariance matrix structure and its applications," 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. 11(3), pages 593-609, September.
    2. Amparo Baíllo & Aurea Grané, 2021. "Subsampling and Aggregation: A Solution to the Scalability Problem in Distance-Based Prediction for Mixed-Type Data," Mathematics, MDPI, vol. 9(18), pages 1-17, September.
    3. Bhat, Chandra R., 2015. "A new generalized heterogeneous data model (GHDM) to jointly model mixed types of dependent variables," Transportation Research Part B: Methodological, Elsevier, vol. 79(C), pages 50-77.
    4. Miguel Angel Ortíz-Barrios & Matias Garcia-Constantino & Chris Nugent & Isaac Alfaro-Sarmiento, 2022. "A Novel Integration of IF-DEMATEL and TOPSIS for the Classifier Selection Problem in Assistive Technology Adoption for People with Dementia," IJERPH, MDPI, vol. 19(3), pages 1-31, January.
    5. 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.

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