Wild bootstrap inference for penalized quantile regression for longitudinal data

The existing theory of penalized quantile regression for longitudinal data has focused primarily on point estimation. In this work, we investigate statistical inference. We propose a wild residual bootstrap procedure and show that it is asymptotically valid approximating the distribution estimator. model puts no restrictions individual effects, estimator achieves consistency by letting shrinkage decay in importance asymptotically. new method easy to implement simulation studies accurate small sample behavior comparison with procedures. Finally, illustrate approach using U.S. Census estimate includes more than eighty thousand parameters.