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Using sliced mean variance–covariance inverse regression for classification and dimension reduction

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  • Charles Lindsey
  • Simon Sheather
  • Joseph McKean

Abstract

The sliced mean variance–covariance inverse regression (SMVCIR) algorithm takes grouped multivariate data as input and transforms it to a new coordinate system where the group mean, variance, and covariance differences are more apparent. Other popular algorithms used for performing graphical group discrimination are sliced average variance estimation (SAVE, targetting the same differences but using a different arrangement for variances) and sliced inverse regression (SIR, which targets mean differences). We provide an improved SMVCIR algorithm and create a dimensionality test for the SMVCIR coordinate system. Simulations corroborating our theoretical results and comparing SMVCIR with the other methods are presented. We also provide examples demonstrating the use of SMVCIR and the other methods, in visualization and group discrimination by k-nearest neighbors. The advantages and differences of SMVCIR from SAVE and SIR are shown clearly in these examples and simulation. Copyright Springer-Verlag Berlin Heidelberg 2014

Suggested Citation

  • Charles Lindsey & Simon Sheather & Joseph McKean, 2014. "Using sliced mean variance–covariance inverse regression for classification and dimension reduction," Computational Statistics, Springer, vol. 29(3), pages 769-798, June.
    • Handle: RePEc:spr:compst:v:29:y:2014:i:3:p:769-798
    DOI: 10.1007/s00180-013-0460-3
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    References listed on IDEAS

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