A semi-parametric quantile regression approach to zero-inflated and incomplete longitudinal outcomes

• Published in: Advances in statistical analysis : AStA : a journal of the German Statistical Society Vol. 104; no. 2; pp. 261 - 283
• Main Authors: Biswas, Jayabrata, Ghosh, Pulak, Das, Kiranmoy
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.06.2020; Springer Nature B.V
• Subjects: Computer simulation; Econometrics; Economics; Finance; Forecasting techniques; Insurance; Management; Markov chains; Mathematics and Statistics; Missing data; Modelling; Normal distribution; Original Paper; Parameter estimation; Probability Theory and Stochastic Processes; Quantiles; Regression analysis; Regression models; Retirement; Statistics; Statistics for Business; Zero-inflated longitudinal data; Health insurance; Missing data; Probit model; Varying coefficients models; Joint quantile models

Quantile regression models are typically used for modeling non-Gaussian outcomes, and such models allow quantile-specific inference. While there exists a vast literature on conditional quantile regression (where the model parameters are estimated precisely for one prefixed quantile level), relativel...