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Authors
Advisor(s)
Abstract(s)
A class of linear classification rules, specifically designed for high-dimensional problems, is
proposed. The new rules are based on Gaussian factor models and are able to incorporate
successfully the information contained in the sample correlations. Asymptotic results, that
allow the number of variables to grow faster than the number of observations, demonstrate
that the worst possible expected error rate of the proposed rules converges to the error
of the optimal Bayes rule when the postulated model is true, and to a slightly larger
constant when this model is a reasonable approximation to the data generating process.
Numerical comparisons suggest that, when combined with appropriate variable selection
strategies, rules derived from one-factor models perform comparably, or better, than the
most successful extant alternatives under the conditions they were designed for. The
proposed methods are implemented as an R package named HiDimDA, available from the
CRAN repository.
Description
Keywords
Discriminant Analysis High dimensionality Expected misclassification rates Microarray classification
Pedagogical Context
Citation
DUARTE SILVA, A.P. - Two-group classification with high-dimensional correlated data: A factor model approach. Computational Statistics and Data Analysis. ISSN 0167-9473. Vol. 55, N.º 11 (2011), p. 2975–2990
Publisher
Elsevier