Maximum likelihood estimation for simplex distribution nonlinear mixed models via the stochastic approximation algorithm

DOI10.1216/RMJ-2008-38-5-1863zbMath1195.62023OpenAlexW2090509382MaRDI QIDQ1011107

Hongjie Wei, Wenzhuan Zhang

Publication date: 7 April 2009

Published in: Rocky Mountain Journal of Mathematics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1216/rmj-2008-38-5-1863

zbMATH Keywords

simplex distributionlongitudinal continuous proportional dataM-H algorithm

Mathematics Subject Classification ID

Applications of statistics to biology and medical sciences; meta analysis (62P10) Point estimation (62F10) Numerical analysis or methods applied to Markov chains (65C40) Stochastic approximation (62L20)

Related Items (2)

Simplex regression models with measurement error ⋮ Proportional inverse Gaussian distribution: A new tool for analysing continuous proportional data

Cites Work

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• Some parametric models on the simplex
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• A stochastic approximation algorithm with Markov chain Monte-Carlo method for incomplete data estimation problems
• Maximum Likelihood Approaches to Variance Component Estimation and to Related Problems
• Maximum Likelihood Variance Components Estimation for Binary Data
• Maximum Likelihood Algorithms for Generalized Linear Mixed Models
• Marginal Models for Longitudinal Continuous Proportional Data
• Maximum-likelihood estimation for the mixed analysis of variance model
• Monte Carlo sampling methods using Markov chains and their applications

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