Likelihood-based approach for analysis of longitudinal nominal data using marginalized random effects models
From MaRDI portal
Publication:3019501
DOI10.1080/02664763.2010.515675zbMath1218.62053OpenAlexW2023783543MaRDI QIDQ3019501
Daekwan Seo, Keunbaik Lee, Xue Feng Liu, Sanggil Kang
Publication date: 28 July 2011
Published in: Journal of Applied Statistics (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1080/02664763.2010.515675
Estimation in multivariate analysis (62H12) Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10)
Related Items
Analysis of long series of longitudinal ordinal data using marginalized models ⋮ Modeling the random effects covariance matrix for generalized linear mixed models ⋮ Analysis of zero-inflated clustered count data: a marginalized model approach ⋮ Marginalized transition shared random effects models for longitudinal binary data with nonignorable dropout
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Longitudinal data analysis using generalized linear models
- Marginalized Transition Models and Likelihood Inference for Longitudinal Categorical Data
- Numerical integration in logistic-normal models
- Analysis of multivariate longitudinal data using quasi-least squares
- Longitudinal nominal data analysis using marginalized models
- Misspecified maximum likelihood estimates and generalised linear mixed models
- Marginally Specified Logistic‐Normal Models for Longitudinal Binary Data
- A likelihood-based method for analysing longitudinal binary responses
- General class of covariance structures for two or more repeated factors in longitudinal data analysis
- A Random-Effects Ordinal Regression Model for Multilevel Analysis
- Likelihood analysis of joint marginal and conditional models for longitudinal categorical data
- Marginalized Models for Moderate to Long Series of Longitudinal Binary Response Data
- Marginal modeling of multilevel binary data with time-varying covariates