The comparison study of the model selection criteria on the Tobit regression model based on the bootstrap sample augmentation mechanisms

DOI10.1080/00949655.2020.1856848OpenAlexW3110650590MaRDI QIDQ5065293

Yue Su, Patrick Kandege Mwanakatwe

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/10.1080/00949655.2020.1856848

zbMATH Keywords

model selectionvariable selectionbootstrap samplinginformation criteriaTobit regression model

Mathematics Subject Classification ID

Statistics (62-XX)

Uses Software

• R
• AER
• censReg

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Estimation of Relationships for Limited Dependent Variables
• Bootstrap-based model selection criteria for beta regressions
• Bootstrap variants of the Akaike information criterion for mixed model selection
• Asymptotic bootstrap corrections of AIC for linear regression models
• Bootstrap methods for standard errors, confidence intervals, and other measures of statistical accuracy
• Estimating the dimension of a model
• Bootstrap methods: another look at the jackknife
• Bootstrapping log likelihood and EIC, an extension of AIC
• Estimating the Error Rate of a Prediction Rule: Improvement on Cross-Validation
• Regression and time series model selection in small samples
• Further analysis of the data by Akaike's information criterion and the finite corrections
• Improvements on Cross-Validation: The .632+ Bootstrap Method
• Regression Analysis when the Dependent Variable Is Truncated Normal
• On Information and Sufficiency
• The bootstrap and Edgeworth expansion

This page was built for publication: The comparison study of the model selection criteria on the Tobit regression model based on the bootstrap sample augmentation mechanisms