Bayesian latent variable models for clustered mixed outcomes

By David B. Dunson. 2000

321 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/72b4cf3034e57bb46bf31dc29955b6b9144fef31

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Citations in the corpus

On Using Bayesian Methods to Address Small Sample Problems (2016, background): “In addition to greater ease of implementation, several recent methodological papers and Monte Carlo simulation studies have noted the potential advantages of Bayesian methods over frequentist maximum likelihood (ML) methods with small samples (e.g., Baldwin & Fellingham, 2013; Depaoli, 2013; Depaoli”
A Bayesian Approach to Multilevel Structural Equation Modeling With Continuous and Dichotomous Outcomes (2015, background): “Moreover, a Bayesian approach is uniquely beneficial in the context of categorical data because it can be used to estimate categorical variable models that cannot be analyzed with currently available frequentist approaches (see, e.g., Ansari & Jedidi, 2000; Dunson, 2000; Muthén & Asparouhov, 2012b; ”
Dynamic Latent Trait Models for Multidimensional Longitudinal Data (2003, methodology): “In addition, methods that allow repeated observations (Dunson 2000) were developedfor clustered data and do not accommodate serial dependency structures.”
Bayesian Structural Equation Modeling (2007, background): “1 2003), and multilevel data (Dunson, 2000; Jedidi and Ansari, 2001).”
Beta-Negative Binomial Process and Poisson Factor Analysis (2011, background): “There has been some work on exponential family latent factor models that incorporate Gaussian latent variables (Dunson, 2000, 2003; Moustaki and Knott, 2000; Sammel et al., 1997), but computation tends to be prohibitive in high-dimensional settings and the Gaussian assumption is restrictive for…”
Strategy for Modelling Nonrandom Missing Data Mechanisms in Observational Studies Using Bayesian Methods (2012, background): “By creating an underlying set of latent variables, models for mixtures of binary, categorical and continuous variables can be developed (Dunson 2000; Goldstein et al. 2009).”
Commentary: practical advantages of Bayesian analysis of epidemiologic data. (2001, methodology): “However, using a Bayesian MCMC approach, a much broader variety of models can be fitted, including those with multilevel correlation structures, different measurement scales for the different test items (e.g., ordered categorical and continuous), and nonlinear regression frameworks (22, 23 ).”
Multilevel models with multivariate mixed response types (2009, methodology): “MCMC procedures are used by Dunson (2000) and Imai and van Dyk (2007) for particular cases of such models.”
Model comparison of nonlinear structural equation models with fixed covariates (2003, unknown): “Nowadays, analysis of SEMs is an area of active research in statistics (see, e.g., Dunson, 2000; Legler & Ryan, 1997; Meng & Schilling,1996; Sammel & Ryan, 1996; Sammel, Ryan, Ledger, 1997; Shi & Lee, 2000; Yuan & Bentler, 1997; among others).”
Bayesian Analysis of Nonlinear Structural Equation Models with Nonignorable Missing Data (2006, background): “(ii) As claimed by many important papers on the Bayesian analysis of SEMs (Ansari, Jedidi, & Dube, 2002; Dunson, 2000; Schines, Hoijtink, & Bromsma, 1999), the sampling-based Bayesian methods do not rely on asymptotic theory and may give reliable statistical inference even with small sample sizes.”