Sensitivity analysis for the identifiability with application to latent random effect model for the mixed data
From MaRDI portal
Publication:5130564
DOI10.1080/02664763.2014.929641OpenAlexW2023017666MaRDI QIDQ5130564
Publication date: 28 October 2020
Published in: Journal of Applied Statistics (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1080/02664763.2014.929641
sensitivity analysisidentifiabilityrandom effect modelsmedical datamixed correlated responsesmaximal normal curvature
Related Items
Joint modeling for longitudinal set-inflated continuous and count responses ⋮ The t linear mixed model: model formulation, identifiability and estimation ⋮ Identifiability of parameters in longitudinal correlated Poisson and inflated beta regression model with non-ignorable missing mechanism ⋮ A note on the identifiability of latent variable models for mixed longitudinal data
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Identifiability conditions for covariate effects model on survival times under informative censoring
- Maximum likelihood estimation of multivariate polyserial and polychoric correlation coefficients
- Linear mixed models in practice: a SAS-oriented approach
- Identifiability of linear mixed effects models
- General mixed-data model: Extension of general location and grouped continuous models
- Distance between populations using mixed continuous and categorical variables
- A Mixed-Model Procedure for Analyzing Ordered Categorical Data
- Response models for mixed binary and quantitative variables
- Dummy Endogenous Variables in a Simultaneous Equation System
- The Grouped Continuous Model for Multivariate Ordered Categorical Variables and Covariate Adjustment
- Regression Models for a Bivariate Discrete and Continuous Outcome with Clustering
- Modelling of repeated ordered measurements by isotonic sequential regression
- Multivariate Correlation Models with Mixed Discrete and Continuous Variables
- A Bayesian Approach for Clustered Longitudinal Ordinal Outcome With Nonignorable Missing Data