A non linear mixed effects model of plant growth and estimation via stochastic variants of the EM algorithm
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Publication:2807795
DOI10.1080/03610926.2014.930909zbMath1341.62310OpenAlexW1999326115MaRDI QIDQ2807795
Charlotte Baey, Paul-Henry Cournède, Samis Trevezas
Publication date: 25 May 2016
Published in: Communications in Statistics - Theory and Methods (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1080/03610926.2014.930909
Applications of statistics to biology and medical sciences; meta analysis (62P10) Monte Carlo methods (65C05)
Related Items (2)
Asymptotic distribution of likelihood ratio test statistics for variance components in nonlinear mixed effects models ⋮ Mixed-effects estimation in dynamic models of plant growth for the assessment of inter-individual variability
Cites Work
- A sequential Monte Carlo approach for MLE in a plant growth model
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- Maximum likelihood estimation in nonlinear mixed effects models
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- Weak convergence and optimal scaling of random walk Metropolis algorithms
- Convergence of the Monte Carlo expectation maximization for curved exponential families.
- Convergence of a stochastic approximation version of the EM algorithm
- Markov chains for exploring posterior distributions. (With discussion)
- Inference in hidden Markov models.
- Some Parameter Estimation Issues in Functional-Structural Plant Modelling
- A Bayesian Approach to Nonlinear Random Effects Models
- Assessing the accuracy of the maximum likelihood estimator: Observed versus expected Fisher information
- Coupling a stochastic approximation version of EM with an MCMC procedure
- Ascent-Based Monte Carlo Expectation– Maximization
- A Stochastic Approximation Method
- An adaptive Metropolis algorithm
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