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A Cautionary Note on Selection of Variables in Discriminant Analysis

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  • Gordon D. Murray

Abstract

When searching for a good subset of variables for use in discriminant analysis, one finds that the apparent error rate does not decrease monotonically with increasing size of subset. This paradox is resolved in terms of bias associated with searching through large numbers of subsets. The explanation throws some doubt onto many established techniques of variable selection.

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  • Gordon D. Murray, 1977. "A Cautionary Note on Selection of Variables in Discriminant Analysis," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 26(3), pages 246-250, November.
    • Handle: RePEc:bla:jorssc:v:26:y:1977:i:3:p:246-250
    DOI: 10.2307/2346964
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    Cited by:

    1. Jun Wang & Mao Li & Martin Skitmore & Jianli Chen, 2024. "Predicting Construction Company Insolvent Failure: A Scientometric Analysis and Qualitative Review of Research Trends," Sustainability, MDPI, vol. 16(6), pages 1-22, March.
    2. Brusco, Michael J. & Steinley, Douglas, 2011. "Exact and approximate algorithms for variable selection in linear discriminant analysis," Computational Statistics & Data Analysis, Elsevier, vol. 55(1), pages 123-131, January.
    3. du Jardin, Philippe & Séverin, Eric, 2012. "Forecasting financial failure using a Kohonen map: A comparative study to improve model stability over time," European Journal of Operational Research, Elsevier, vol. 221(2), pages 378-396.
    4. Duarte Silva, António Pedro, 2001. "Efficient Variable Screening for Multivariate Analysis," Journal of Multivariate Analysis, Elsevier, vol. 76(1), pages 35-62, January.
    5. Brusco, Michael J., 2014. "A comparison of simulated annealing algorithms for variable selection in principal component analysis and discriminant analysis," Computational Statistics & Data Analysis, Elsevier, vol. 77(C), pages 38-53.

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