Bayesian Estimation of the Log–linear Exponential Regression Model with Censorship and Collinearity

DOI10.1080/03610918.2013.857686zbMath1341.62265OpenAlexW2067361315MaRDI QIDQ2809591

Flaviano Godínez-Jaimes, Ramón Reyes-Carreto, Francisco J. Ariza Hernandez

Publication date: 30 May 2016

Published in: Communications in Statistics - Simulation and Computation (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/03610918.2013.857686

zbMATH Keywords

simulation studyMetropolis algorithmcensorshipcollinearitycoverageslog-linear regressionBayes' estimator

Mathematics Subject Classification ID

Censored data models (62N01) Bayesian inference (62F15)

Cites Work

• Unnamed Item
• A weakly informative default prior distribution for logistic and other regression models
• The general problem of ill conditioning and its role in statistical analysis
• Improved ridge regression estimators for the logistic regression model
• A New Estimator for Cox Proportional Hazard Regression Model in Presence of Collinearity
• On Ridge Parameters in Logistic Regression
• Collinearity in generalized linear models
• A multicollinearity diagnostic for models fit to censored data
• On Some Ridge Regression Estimators: An Empirical Comparisons
• Ridge estimation in regression problems with autocorrelated errors: A monte carlo study
• Regresion analysis with multicollinear predictor variables: definition, derection, and effects
• Choosing Ridge Parameter for Regression Problems
• Using Liu-Type Estimator to Combat Collinearity
• Adaptive Rejection Sampling for Gibbs Sampling
• Bayesian Inference for Generalized Linear and Proportional Hazards Models via Gibbs Sampling
• Performance of Some Ridge Parameters for Probit Regression: With Application to Swedish Job Search Data
• Modified Ridge Regression Estimators
• Liu-Type Logistic Estimator
• A multicollinearity diagnostic for the cox model with time dependent covariates
• Ridge Regression – A Simulation Study

This page was built for publication: Bayesian Estimation of the Log–linear Exponential Regression Model with Censorship and Collinearity