Very sorry about my incomplete email from 2 days ago, here is the full version. ----------------------------------------------------------------------- Dear all, I have read various descriptions of employing resampling techniques, such as the bootstrap, to estimate the uncertainties of the eigenvalues/vectors computed by PCA. Let's say I define my test statistic T to be the percent of variance captured by the first 2 principal components. I am struggling with a conceptual issue here. Since PCA maximizes the variance concentration, wouldn't the bootstrapped distribution of T be biased upward due to the effectively reduced sample size ? If that is true, how could one obtain an unbiased confidence interval for T ?
Any insight would be helpful ! Thanks, Markus ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
