  In the context of principal components analysis (PCA), the bootstrap is
commonly applied to solve a variety of inference problems, such as constructing
confidence intervals for the eigenvalues of the population covariance matrix
$\Sigma$. However, when the data are high-dimensional, there are relatively few
theoretical guarantees that quantify the performance of the bootstrap. Our aim
in this paper is to analyze how well the bootstrap can approximate the joint
distribution of the leading eigenvalues of the sample covariance matrix
$\hat\Sigma$, and we establish non-asymptotic rates of approximation with
respect to the multivariate Kolmogorov metric. Under certain assumptions, we
show that the bootstrap can achieve the dimension-free rate of
${\tt{r}}(\Sigma)/\sqrt n$ up to logarithmic factors, where ${\tt{r}}(\Sigma)$
is the effective rank of $\Sigma$, and $n$ is the sample size. From a
methodological standpoint, our work also illustrates that applying a
transformation to the eigenvalues of $\hat\Sigma$ before bootstrapping is an
important consideration in high-dimensional settings.
