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Bayesian latent class models for the multiple imputation of cross-sectional, multilevel and longitudinal categorical data
Davide Vidotto
Methodology and Statistics
Research output
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Thesis
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Doctoral Thesis
336
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Dive into the research topics of 'Bayesian latent class models for the multiple imputation of cross-sectional, multilevel and longitudinal categorical data'. Together they form a unique fingerprint.
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Keyphrases
Missing Data
100%
Multiple Imputation
100%
Longitudinal Categorical Data
100%
Bayesian Latent Class Model
100%
Statistical Analysis
71%
Latent Class Model
57%
Longitudinal Data
42%
Non-response
14%
Latent Class
14%
Hierarchical Structure
14%
Latent Markov Model
14%
Lack of Information
14%
Categorical Data
14%
Multilevel Structure
14%
Missing Categorical Data
14%
Multilevel Latent Class Models
14%
Imputed Values
14%
Time Lead
14%
Imputing Missing Data
14%
Data Collection Design
14%
Longitudinal Structure
14%
Complete Representation
14%
Possible Answer
14%
Mathematics
Bayesian
100%
Statistical Analysis
100%
Multiple Imputation
100%
Categorical Data
100%
Longitudinal Data
60%
Probability Theory
40%
Observed Value
40%
Nonresponse
20%
Observed Data
20%
Imputed Value
20%