no code implementations • 16 Mar 2023 • Joel L. Horowitz, Ahnaf Rafi
This paper gives conditions under which the bootstrap, based on estimates obtained through SCAD penalization with thresholding, provides asymptotic refinements of size \(O \left( n^{- 2} \right)\) for the error in the rejection (coverage) probability of a symmetric hypothesis test (confidence interval) and \(O \left( n^{- 1} \right)\) for the error in the rejection (coverage) probability of a one-sided or equal tailed test (confidence interval).
no code implementations • 21 Jul 2022 • Guohao Shen, Yuling Jiao, Yuanyuan Lin, Joel L. Horowitz, Jian Huang
We propose a penalized nonparametric approach to estimating the quantile regression process (QRP) in a nonseparable model using rectifier quadratic unit (ReQU) activated deep neural networks and introduce a novel penalty function to enforce non-crossing of quantile regression curves.
no code implementations • 2 Mar 2018 • Federico A. Bugni, Joel L. Horowitz
The paper presents a method for testing the hypothesis that the same stochastic process generates all the functional data.