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 derived using summary.rq's "nid" option. The outcome variable is severely skewed, and I am afraid that this may be affecting the consistency of the bootstrap variance estimates. I have read that the m out of n bootstrap can be used to overcome this problem. However, this procedure requires both the original sample (n) and the subsample (m) sizes to be large. The version implemented in rq.boot does not appear to provide any improvement over the naive bootstrap. Ultimately, I am interested in using median regression to model changes in the outcome variable over time. Summary.rq's robust variance estimator is not applicable to repeated-measures data. I question whether the block (cluster) bootstrap variance estimator, which can accommodate intraclass correlation, would perform well. Can anyone suggest alternatives for variance estimation in this situation?
Regards, Jim James W. Shaw, Ph.D., Pharm.D., M.P.H. Assistant Professor Department of Pharmacy Administration College of Pharmacy University of Illinois at Chicago 833 South Wood Street, M/C 871, Room 266 Chicago, IL 60612 Tel.: 312-355-5666 Fax: 312-996-0868 Mobile Tel.: 215-852-3045 ______________________________________________ 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.
