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Discrimination and Allocation with Discrete Data

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  • M. Hills

Abstract

Two problems are considered: (a) Selecting variables which discriminate well between two or more populations. (6) Estimating log‐likelihood ratios for a particular subject who must be allocated to one or other of the populations. The data on each subject are assumed to be discrete. It is suggested that the use of error rates as a measure of discrimination is unsatisfactory in this situation and an alternative measure is proposed.

Suggested Citation

  • M. Hills, 1967. "Discrimination and Allocation with Discrete Data," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 16(3), pages 237-250, November.
    • Handle: RePEc:bla:jorssc:v:16:y:1967:i:3:p:237-250
    DOI: 10.2307/2985920
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    Cited by:

    1. Margarita Díaz & Fernando Ferrero & Cecilia Díaz & Patricia Caro & María Inés Stimolo, 2005. "Análisis del desempleo urbano a través de un estudio comparativo de métodos de clasificación," Revista de Economía y Estadística, Universidad Nacional de Córdoba, Facultad de Ciencias Económicas, Instituto de Economía y Finanzas, vol. 43(2), pages 61-85, Diciembre.
    2. Asparoukhov, Ognian K. & Krzanowski, Wojtek J., 2001. "A comparison of discriminant procedures for binary variables," Computational Statistics & Data Analysis, Elsevier, vol. 38(2), pages 139-160, December.
    3. Mkhadri, A. & Celeux, G. & Nasroallah, A., 1997. "Regularization in discriminant analysis: an overview," Computational Statistics & Data Analysis, Elsevier, vol. 23(3), pages 403-423, January.

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