  We propose an estimation method for the conditional mode when the
conditioning variable is high-dimensional. In the proposed method, we first
estimate the conditional density by solving quantile regressions multiple
times. We then estimate the conditional mode by finding the maximum of the
estimated conditional density. The proposed method has two advantages in that
it is computationally stable because it has no initial parameter dependencies,
and it is statistically efficient with a fast convergence rate. Synthetic and
real-world data experiments demonstrate the better performance of the proposed
method compared to other existing ones.
