Composite likelihood and maximum likelihood methods for joint latent class modeling of disease prevalence and high-dimensional semicontinuous biomarker data
DOI10.1007/s00180-015-0597-3zbMath1342.65076OpenAlexW833529646MaRDI QIDQ736631
Zhiwei Zhang, Hui Zhang, Wei Liu, Bo Zhang, Qihui Chen
Publication date: 4 August 2016
Published in: Computational Statistics (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1007/s00180-015-0597-3
Markov chain Monte Carloexpectation-maximization algorithmpseudo-likelihoodshared latent class modelstwo-part models
Computational methods for problems pertaining to statistics (62-08) Classification and discrimination; cluster analysis (statistical aspects) (62H30) Applications of statistics to biology and medical sciences; meta analysis (62P10)
Related Items (1)
Uses Software
Cites Work
- Unnamed Item
- Unnamed Item
- A pairwise likelihood approach to estimation in multilevel probit models
- Models for discrete longitudinal data.
- Latent class models for joint analysis of disease prevalence and high-dimensional semicontinuous biomarker data
- A pairwise likelihood approach to generalized linear models with crossed random effects
- Maximizing Generalized Linear Mixed Model Likelihoods With an Automated Monte Carlo EM Algorithm
- Maximum Likelihood Estimation for Probit-Linear Mixed Models with Correlated Random Effects
- Variance component testing in generalised linear models with random effects
- Maximum Likelihood Algorithms for Generalized Linear Mixed Models
- A Two-Part Random-Effects Model for Semicontinuous Longitudinal Data
- Crossed Random Effect Models for Multiple Outcomes in a Study of Teratogenesis
- A Limited Memory Algorithm for Bound Constrained Optimization
This page was built for publication: Composite likelihood and maximum likelihood methods for joint latent class modeling of disease prevalence and high-dimensional semicontinuous biomarker data