2017/12/23 by Hirofumi Ohta, Ohta, Hirofumi, Satoshi Hara +1
Engineering · Mathematics · #Advanced Statistical Methods and Models #Control Systems and Identification #FOS: Computer and information sciences #FOS: Mathematics #Machine Learning (stat.ML) #Methodology (stat.ME) #Statistical Methods and Inference #Statistics Theory (math.ST)
paper · pdf · doi:10.48550/arxiv.1712.08754
openalex publication_date 2017/12/23 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.