Estimating confidence intervals for principal component analysis: A comparison between the bootstrap and asymptotic results.

Confidence intervals (CIs) in principal component analysis (PCA) can be based on asymptotic standard errors and on the bootstrap methodology. The present paper offers an overview of possible strategies for bootstrapping in PCA. A motivating example shows that CI estimates for the component loadings using different methods may diverge. We explain that this results from both differences in quality and in perspective on the rotational freedom of the population loadings. A comparative simulation study examines the quality of various estimated component loading CIs. The bootstrap approach is more flexible and generally yields better CIs than the asymptotic approach. However, in the case of a clear simple structure of varimax rotated loadings, one can be confident that the asymptotic estimates are reasonable as well.