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A Monte Carlo Comparison of Two Linear Dimension Reduction Matrices for Statistical Discrimination
Volume 3, Issue 4 (2005), pp. 449–464
J. Wade Davis   Dean M. Young   Karin B. Ernstrom-Keim  

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https://doi.org/10.6339/JDS.2005.03(4).227
Pub. online: 20 July 2021      Type: Research Article      Open accessOpen Access

Published
20 July 2021

Abstract

Abstract: We compare two linear dimension-reduction methods for statisti cal discrimination in terms of average probabilities of misclassification in re duced dimensions. Using Monte Carlo simulation we compare the dimension reduction methods over several different parameter configurations of multi variate normal populations and find that the two methods yield very different results. We also apply the two dimension-reduction methods examined here to data from a study on football helmet design and neck injuries.

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Journal of data science

  • Online ISSN: 1683-8602
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