The goal of regression analysis is to describe the stochastic relationship between an input vector x and a scalar output y . This can be achieved by estimating the entire conditional density p ( y ∣ x ). In this letter, we present a new approach for nonparametric conditional density estimation. We develop a piecewise-linear path-following method for kernel-based quantile regression. It enables us to estimate the cumulative distribution function of p ( y ∣ x ) in piecewise-linear form for all x in the input domain. Theoretical analyses and experimental results are presented to show the effectiveness of the approach.