Abstract
We consider a conditional quantile regression model for spatial data. More precisely, given a strictly stationary random field Z(i) = (X-i, Y-i)(i subset of N)(N), we investigate a kernel estimate of the conditional quantile regression function of the univariate response variable Y-i given the functional variable X-i. The main purpose of the paper is to prove the convergence (with rate) in L-p norm and the asymptotic normality of the estimator.