  In this paper, we consider estimation of the conditional mode of an outcome
variable given regressors. To this end, we propose and analyze a
computationally scalable estimator derived from a linear quantile regression
model and develop asymptotic distributional theory for the estimator.
Specifically, we find that the pointwise limiting distribution is a scale
transformation of Chernoff's distribution despite the presence of regressors.
In addition, we consider analytical and subsampling-based confidence intervals
for the proposed estimator. We also conduct Monte Carlo simulations to assess
the finite sample performance of the proposed estimator together with the
analytical and subsampling confidence intervals. Finally, we apply the proposed
estimator to predicting the net hourly electrical energy output using Combined
Cycle Power Plant Data.
