Quantile regression for nonlinear mixed effects models: a likelihood based perspective

DOI10.1007/s00362-018-0988-yzbMath1443.62096OpenAlexW2790007761MaRDI QIDQ779702

Christian E. Galarza, Francisco Louzada, Victor Hugo Lachos, Luis Mauricio Castro

Publication date: 14 July 2020

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

Full work available at URL: https://doi.org/10.1007/s00362-018-0988-y

zbMATH Keywords

quantile regressionasymmetric Laplace distributionnonlinear mixed effects modelsstochastic approximation of the EM algorithm (SAEM algorithm)

Mathematics Subject Classification ID

Nonparametric regression and quantile regression (62G08) Linear regression; mixed models (62J05) Applications of statistics to biology and medical sciences; meta analysis (62P10) Exact distribution theory in statistics (62E15) Stochastic approximation (62L20)

Related Items (4)

Bayesian nonparametric quantile mixed-effects models via regularization using Gaussian process priors ⋮ Flexible Bayesian quantile regression for nonlinear mixed effects models based on the generalized asymmetric Laplace distribution ⋮ Quantile regression in random effects meta-analysis model ⋮ Approximate inferences for nonlinear mixed effects models with scale mixtures of skew-normal distributions

Uses Software

• R
• S-PLUS
• MEMSS
• qrNLMM

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Linear quantile mixed models
• Multitude of Laplace distributions
• Construction of Bayesian deformable models via a stochastic approximation algorithm: a convergence study
• Quantile regression for longitudinal data with a working correlation model
• Quantile regression for dynamic panel data with fixed effects
• Estimation in nonlinear mixed-effects models using heavy-tailed distributions
• Posterior consistency of Bayesian quantile regression based on the misspecified asymmetric Laplace density
• Maximum likelihood estimation in nonlinear mixed effects models
• Penalized quantile regression for dynamic panel data
• Bayesian analysis of penalized quantile regression for longitudinal data
• Quantile regression in varying-coefficient models: non-crossing quantile curves and heteroscedasticity
• Bayesian inference in nonlinear mixed-effects models using normal independent distributions
• Convergence of a stochastic approximation version of the EM algorithm
• Quantile regression for longitudinal data
• Bayesian quantile regression for parametric nonlinear mixed effects models
• Non-Gaussian Ornstein–Uhlenbeck-based Models and Some of Their Uses in Financial Economics
• A Bayesian Approach to Joint Mixed-Effects Models with a Skew-Normal Distribution and Measurement Errors in Covariates
• Quantile regression for longitudinal data using the asymmetric Laplace distribution
• Bayesian Quantile Regression for Longitudinal Studies with Nonignorable Missing Data
• Mixed Effects Models for Complex Data
• Maximizing Generalized Linear Mixed Model Likelihoods With an Automated Monte Carlo EM Algorithm
• Quantile Regression Methods for Longitudinal Data with Drop-outs: Application to CD4 Cell Counts of Patients Infected with the Human Immunodeficiency Virus
• A Joint Model for Nonlinear Mixed-Effects Models With Censoring and Covariates Measured With Error, With Application to AIDS Studies
• Mixed-Effects Models in S and S-PLUS
• Coupling a stochastic approximation version of EM with an MCMC procedure
• Equation of State Calculations by Fast Computing Machines
• Power Transformation Toward a Linear Regression Quantile
• Monte Carlo sampling methods using Markov chains and their applications
• A Three-Parameter Asymmetric Laplace Distribution and Its Extension
• Bayesian quantile regression

This page was built for publication: Quantile regression for nonlinear mixed effects models: a likelihood based perspective