Penalty and Shrinkage Strategies Based on Local Polynomials for Right-Censored Partially Linear Regression

Penalty and Shrinkage Strategies Based on Local Polynomials for Right-Censored Partially Linear Regression

This study aims to propose modified semiparametric estimators based on six different penalty and shrinkage strategies for the estimation of a right-censored semiparametric regression model. In this context, the methods used to obtain the estimators are ridge, lasso, adaptive lasso, SCAD, MCP, and el...

Full description

Bibliographic Details
Main Authors: Syed Ejaz Ahmed, Dursun Aydın, Ersin Yılmaz
Format: Article
Language:English
Published: MDPI AG 2022-12-01
Series:Entropy
Subjects:
local polynomial regression
lasso
elasticnet
SCAD
MCP
partially linear model
Online Access:https://www.mdpi.com/1099-4300/24/12/1833

Internet

https://www.mdpi.com/1099-4300/24/12/1833

Similar Items