A Bayesian method for analyzing combinations of continuous, ordinal, and nominal categorical data with missing values
From MaRDI portal
Publication:2256744
DOI10.1016/j.jmva.2014.11.007zbMath1329.62038OpenAlexW1967292558MaRDI QIDQ2256744
W. John Boscardin, Yulei He, Xiaohai Wan, Thomas R. Belin, Kui Zhang, Xiao Zhang
Publication date: 20 February 2015
Published in: Journal of Multivariate Analysis (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1016/j.jmva.2014.11.007
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Cites Work
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- Longitudinal data analysis using generalized linear models
- Random-Effects Models for Longitudinal Data
- Bayesian analysis of multivariate nominal measures using multivariate multinomial probit models
- Statistical inference in the multinomial multiperiod probit model
- A Bayesian analysis of the multinomial probit model with fully identified parameters
- Inference from iterative simulation using multiple sequences
- Generalized latent trait models
- Latent Transition Regression for Mixed Outcomes
- Markov Chain Monte Carlo Convergence Diagnostics: A Comparative Review
- Models for Longitudinal Data: A Generalized Estimating Equation Approach
- Correlated Binary Regression with Covariates Specific to Each Binary Observation
- Simulation Run Length Control in the Presence of an Initial Transient
- Longitudinal profiling of health care units based on continuous and discrete patient outcomes
- General mixed-data model: Extension of general location and grouped continuous models
- Modeling a mixture of ordinal and continuous repeated measures
- Maximum likelihood estimation for mixed continuous and categorical data with missing values
- The Calculation of Posterior Distributions by Data Augmentation
- A Class of Logistic Regression Models for Multivariate Binary Time Series
- Analysis of multivariate probit models
- Ellipsoidally symmetric extensions of the general location model for mixed categorical and continuous data
- Regression Models for Mixed Discrete and Continuous Responses with Potentially Missing Values
- Nonconjugate Bayesian Estimation of Covariance Matrices and Its Use in Hierarchical Models
- A Method of Simulated Moments for Estimation of Discrete Response Models Without Numerical Integration
- The Analysis of Longitudinal Polytomous Data: Generalized Estimating Equations and Connections with Weighted Least Squares
- Efficiency and Power of Tests for Multiple Binary Outcomes
- Latent Variable Models for Clustered Ordinal Data
- Latent Variable Models with Mixed Continuous and Polytomous Data
- Multilevel models with multivariate mixed response types
- Bayesian Analysis of Binary and Polychotomous Response Data
- Multivariate Correlation Models with Mixed Discrete and Continuous Variables
- Estimation of a Two-Equation Panel Model with Mixed Continuous and Ordered Categorical Outcomes and Missing Data
- Comment on article by Jain and Neal
