Multiple Imputation of Missing Categorical and Continuous Values via Bayesian Mixture Models With Local Dependence
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Summary
A nonparametric Bayesian joint model for multivariate continuous and categorical variables and imputations based on the proposed model tend to have better repeated sampling properties than the default application of chained equations in this realistic setting.
- Type
- article
- Published
- 2014-10-02
- Cited by
- 97
- References
- 52
- Access
- Open access
- OpenAlex
- https://openalex.org/W92430206
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:35110211
Keywords
Categorical variable, Missing data, Statistics, Multivariate statistics, Imputation (statistics)
References
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- Analysis of Complex Survey Samples
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- An ANOVA Model for Dependent Random Measures
- Missing-Data Adjustments in Large Surveys
- Multiple Imputation of Missing or Faulty Values Under Linear Constraints
- An enriched conjugate prior for Bayesian nonparametric inference
- INFERENCE AND MISSING DATA
- Semi-parametric Selection Models for Potentially Non-ignorable Attrition in Panel Studies with Refreshment Samples
- Maximum likelihood estimation for mixed continuous and categorical data with missing values
- ON THE STATIONARY DISTRIBUTION OF ITERATIVE IMPUTATIONS
- The Multiple Adaptations of Multiple Imputation
- Multivariate Correlation Models with Mixed Discrete and Continuous Variables
- A conditional model for incomplete covariates in parametric regression models
- Latent class based multiple imputation approach for missing categorical data.
- Missing covariates in generalized linear models when the missing data mechanism is non‐ignorable
- Gibbs Sampling Methods for Stick-Breaking Priors
Cited by
- Dirichlet Process Mixture Models for Nested Categorical Data
- Nonparametric Bayes Analysis of Social Science Data
- An Empirical Comparison of Multiple Imputation Methods for Categorical Data
- Maximum Likelihood for Social Science
- Bayesian Mixture Models with Focused Clustering for Mixed Ordinal and Nominal Data
- Stop or Continue Data Collection: A Nonignorable Missing Data Approach for Continuous Variables
- Sequential BART for imputation of missing covariates.
- Dirichlet Process Mixture Models for Modeling and Generating Synthetic Versions of Nested Categorical Data
- Non‐parametric Bayes models for mixed scale longitudinal surveys
- Bayesian inference on group differences in multivariate categorical data
- Imputation multiple par analyse factorielle : Une nouvelle méthodologie pour traiter les données manquantes
- Bayesian Mixture Modeling for Multivariate Conditional Distributions
- Nonparametric Bayesian Models With Focused Clustering for Mixed Ordinal and Nominal Data
- Guided Bayesian imputation to adjust for confounding when combining heterogeneous data sources in comparative effectiveness research
- Bayesian-based parallel ant system for missing value estimation in large databases
- Clustering and Variable Selection in the Presence of Mixed Variable Types and Missing Data
- Prediction and Inference With Missing Data in Patient Alert Systems
- Heuristically repopulated Bayesian ant colony optimization for treating missing values in large databases
- Estimation of incomplete values in heterogeneous attribute large datasets using discretized Bayesian max–min ant colony optimization
- Efficient Bayesian Nonparametric Inference for Categorical Data with General High Missingness
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