A Bayesian quantile regression approach to multivariate semi-continuous longitudinal data

• Published in: Computational statistics Vol. 36; no. 1; pp. 241 - 260
• Main Authors: Biswas, Jayabrata, Das, Kiranmoy
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.03.2021; Springer Nature B.V
• Subjects: Algorithms; Bayesian analysis; Economic Theory/Quantitative Economics/Mathematical Methods; Estimation; Expenditures; Mathematics and Statistics; Modelling; Multivariate analysis; Original Paper; Parameter estimation; Probability and Statistics in Computer Science; Probability Theory and Stochastic Processes; Quantiles; Regression analysis; Retirement; Skewed distributions; Statistical analysis; Statistics; MCMC; Zero-inflation; Quantile regression; Tobit models; Longitudinal response; Mixed model

Quantile regression is a powerful tool for modeling non-Gaussian data, and also for modeling different quantiles of the probability distributions of the responses. We propose a Bayesian approach of estimating the quantiles of multivariate longitudinal data where the responses contain excess zeros. W...