On Estimation of Conditional Modes Using Multiple Quantile Regressions

Abstract

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.

26 pages, 3 figures

References in corpus (5)

• Nonparametric modal regression
• Uniform Inference for High-dimensional Quantile Regression: Linear Functionals and Regression Rank Scores
• A Statistical Learning Approach to Modal Regression
• Distributed inference for quantile regression processes
• Valid Post-Selection Inference in High-Dimensional Approximately Sparse Quantile Regression Models