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Quadratic location discriminant functions for mixed categorical and continuous data

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  • Krzanowski, W. J.

Abstract

The assumption of common within-cell dispersion matrices in the location model for mixtures of categorical and continuous variables is relaxed, to allow different matrices in the two populations being discriminated. This extension changes the Bayes location rule from a choice among linear functions to a choice among quadratic functions, but most of the previous methodology can be used directly in the extended case. One extra algebraic identity is provided to assist in the leave-one-out estimation of error rates for assessment of the rule, and the benefit of the extension is illustrated on a medical data set.

Suggested Citation

  • Krzanowski, W. J., 1994. "Quadratic location discriminant functions for mixed categorical and continuous data," Statistics & Probability Letters, Elsevier, vol. 19(2), pages 91-95, January.
    • Handle: RePEc:eee:stapro:v:19:y:1994:i:2:p:91-95
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    Cited by:

    1. 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.
    2. Chi-Ying Leung, 2001. "Error rates in classification consisting of discrete and continuous variables in the presence of covariates," Statistical Papers, Springer, vol. 42(2), pages 265-273, April.

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