Penalized Pairwise Difference Estimation for a High-Dimensional Censored Regression Model

Recommend Faculti to your librarian, HOD, or research lead.

Empirical economics employs high-dimensional data more, says research. This article discusses high-dimensional censored linear regression estimation and model selection. A new l1-penalized pairwise difference least absolute deviations (LAD) estimator and a post-penalized estimator that improves convergence rate is proposed. Simulations reveal faster calculation time and good statistical performance for the proposed estimators.

Image courtesy of interviewee

Read the Study

Leave a Reply

Your email address will not be published.

Copyright © Faculti Media Limited 2013 - 2024. All rights reserved.
error: