Online Program Home
  My Program

All Times EDT

Abstract Details

Activity Number: 520 - New Quantile-Modeling Methods for Large-Scale Heterogeneous Data
Type: Invited
Date/Time: Thursday, August 6, 2020 : 1:00 PM to 2:50 PM
Sponsor: Section on Nonparametric Statistics
Abstract #309332
Title: Quantile Regression Approach to Conditional Mode Estimation
Author(s): Kengo Kato* and Hirofumi Ota and Satoshi Hara
Companies: Cornell University and Rutgers University and Osaka University
Keywords: Chernoff's distribution; cube root asymptotics; modal function; quantile function
Abstract:

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.


Authors who are presenting talks have a * after their name.

Back to the full JSM 2020 program