Description:
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.