Random projections for quantile ridge regression

Quantile regression estimate gives more complete information about the response distribution but is more costly to compute than mean regression. When the dimension is large, a ridge penalty is conventionally used to stabilize the estimate and achieve better bias-variance trade-off. We investigate a random projection approach to ease the computational burden and establish its statistical properties. Monte Carlo studies are carried out to illustrate the computational and statistical properties of the estimates.