Robust beta regression modeling with errors-in-variables: a Bayesian approach and numerical applications

DOI10.1007/s00362-021-01260-1OpenAlexW3199367569MaRDI QIDQ2151693

Jorge I. Figueroa-Zúñiga, Cristian L. Bayes, Víctor Leiva, Shuangzhe Liu

Publication date: 5 July 2022

Published in: Statistical Papers (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s00362-021-01260-1

zbMATH Keywords

regression analysisMonte Carlo simulationBayesian statisticsmeasurement errorsstatistical software

Mathematics Subject Classification ID

Statistics (62-XX)

Related Items (2)

A novel claim size distribution based on a Birnbaum-Saunders and gamma mixture capturing extreme values in insurance: estimation, regression, and applications ⋮ The r‐hypergeometric distribution: Characterization, mathematical methods, simulations, and applications in sciences and engineering

Uses Software

• Stan
• NUTS

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Beta Regression for Modelling Rates and Proportions
• The No-U-Turn Sampler: Adaptively Setting Path Lengths in Hamiltonian Monte Carlo
• Mixed beta regression: a Bayesian perspective
• Modeling heavy-tailed, skewed and peaked uncertainty phenomena with bounded support
• Stochastic restricted estimation in partially linear additive errors-in-variables models
• A survival model with Birnbaum-Saunders frailty for uncensored and censored cancer data
• Elliptical linear mixed models with a covariate subject to measurement error
• Mixture densities for project management activity times: A robust approach to PERT
• A new robust regression model for proportions
• Bayesian inference for generalized linear mixed models
• An Objective Bayesian Criterion to Determine Model Prior Probabilities
• Measurement Error
• Optimal Scaling of Discrete Approximations to Langevin Diffusions
• Missing Covariates in Generalized Linear Models When the Missing Data Mechanism is Non-ignorable
• Mixture Models, Robustness, and the Weighted Likelihood Methodology
• Bayesian Measures of Model Complexity and Fit
• Errors-in-variables beta regression models
• Measurement Error in Nonlinear Models
• Augmented-limited regression models with an application to the study of the risk perceived using continuous scales
• Log‐symmetric quantile regression models

This page was built for publication: Robust beta regression modeling with errors-in-variables: a Bayesian approach and numerical applications