Maximum likelihood estimation of nonlinear mixed-effects models with crossed random effects by combining first-order conditional linearization and sequential quadratic programming
DOI10.1142/S1793524519500402zbMath1419.92012OpenAlexW2942354878MaRDI QIDQ5228122
Liyong Fu, Xinyu Song, Zuoheng Wang, Shouzheng Tang, Ming-Liang Wang
Publication date: 9 August 2019
Published in: International Journal of Biomathematics (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1142/s1793524519500402
sequential quadratic programmingnonlinear mixed-effects modelscrossed random effectsnested random effectsfirst-order conditional expansion
Applications of statistics to biology and medical sciences; meta analysis (62P10) Quadratic programming (90C20) Plant biology (92C80)
Uses Software
Cites Work
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- Random-Effects Models for Longitudinal Data
- Two Taylor-series approximation methods for nonlinear mixed models
- Parameter estimation of two-level nonlinear mixed effects models using first order conditional linearization and the EM algorithm
- Extension of the SAEM algorithm for nonlinear mixed models with 2 levels of random effects
- Monte Carlo Comparison of ANOVA, MIVQUE, REML, and ML Estimators of Variance Components
- Multivariate Multilevel Nonlinear Mixed Effects Models for Timber Yield Predictions
- The Sequential Quadratic Programming Method
- Multilevel covariance component models
- Newton-Raphson and EM Algorithms for Linear Mixed-Effects Models for Repeated-Measures Data
- Maximum Likelihood Approaches to Variance Component Estimation and to Related Problems
- Computing Gaussian Likelihoods and Their Derivatives for General Linear Mixed Models
- Mixed-Effects Models in S and S-PLUS
- Generalized Linear Models with Random Effects
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