Asymptotic properties of two-group supervised classi cation rules designed for problems with
much more variables than observations are discussed. Two types of asymptotic bounds on expected
error rates are considered: (i) bounds that assume consistent mean estimators and focus on the impact
of the covariance matrix estimation. (ii) bounds that consider the errors in mean and covariance
estimation. Known results for independence-based classi cation rules are generalized to correlationadjusted
linear rules.info:eu-repo/semantics/publishedVersio
