  This paper considers an estimation of semiparametric functional
(varying)-coefficient quantile regression with spatial data. A general robust
framework is developed that treats quantile regression for spatial data in a
natural semiparametric way. The local M-estimators of the unknown
functional-coefficient functions are proposed by using local linear
approximation, and their asymptotic distributions are then established under
weak spatial mixing conditions allowing the data processes to be either
stationary or nonstationary with spatial trends. Application to a soil data set
is demonstrated with interesting findings that go beyond traditional analysis.
