Re: [R] Robust variance estimation with rq (failure of the bootstrap?)

Matt: Thanks for your prompt reply. The disparity between the bootstrap and sandwich variance estimates derived when modeling the highly skewed outcome suggest that either (A) the empirical robust variance estimator is underestimating the variance or (B) the bootstrap is breaking down. The

Re: [R] Robust variance estimation with rq (failure of the bootstrap?)

Jim, Thanks for pointing me to this article. The authors argue that the bootstrap intervals for a robust estimator may not be as robust as the estimator. In this context, robustness is measured by the breakdown point, which is supposed to measure robustness to outliers. Even so, the authors

[R] Robust variance estimation with rq (failure of the bootstrap?)

I am fitting quantile regression models using data collected from a sample of 124 patients. When modeling cross-sectional associations, I have noticed that nonparametric bootstrap estimates of the variances of parameter estimates are much greater in magnitude than the empirical Huber estimates

Re: [R] Robust variance estimation with rq (failure of the bootstrap?)

Jim, If repeated measurements on patients are correlated, then resampling all measurements independently induces an incorrect sampling distribution (= incorrect variance) on a statistic of these data. One solution, as you mention, is the block or cluster bootstrap, which preserves the