A new biased estimation method in tobit regression: theory and application

DOI10.1080/00949655.2020.1845699OpenAlexW3099542448MaRDI QIDQ5065243

Esra Öğütcüoğlu, Yasin Asar

Publication date: 18 March 2022

Published in: Journal of Statistical Computation and Simulation (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/00949655.2020.1845699

zbMATH Keywords

mean squared errorMonte Carlo simulationmulticollinearityLiu estimatortobit model

Mathematics Subject Classification ID

Ridge regression; shrinkage estimators (Lasso) (62J07) Linear regression; mixed models (62J05) Statistics (62-XX)

Uses Software

• R

Cites Work

• Unnamed Item
• Unnamed Item
• Estimation of Relationships for Limited Dependent Variables
• Liu estimation in generalized linear models: application on gamma distributed response variable
• Logistic Liu estimator under stochastic linear restrictions
• Linear models and generalizations. Least squares and alternatives. With contributions by Michael Schomaker.
• A Tobit Ridge Regression Estimator
• Collinearity in generalized linear models
• A Monte Carlo Evaluation of Some Ridge-Type Estimators
• A new class of blased estimate in linear regression
• Performance of Some New Ridge Regression Estimators
• On the performance of some new Liu parameters for the gamma regression model
• Developing a Liu estimator for the negative binomial regression model: method and application
• Ridge Regression: Biased Estimation for Nonorthogonal Problems

This page was built for publication: A new biased estimation method in tobit regression: theory and application