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Informed strategies for multivariate missing data
Mingyang Cai
Methodology and statistics for the behavioural and social sciences
Leerstoel van Buuren
Research output
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Thesis
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Doctoral thesis 1 (Research UU / Graduation UU)
Overview
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Dive into the research topics of 'Informed strategies for multivariate missing data'. Together they form a unique fingerprint.
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Keyphrases
Fully Conditional Specification
100%
Multivariate Missing Data
100%
Imputation Methods
36%
Joint Modeling
36%
Hybrid Imputation
36%
Imputation Model
27%
Predictive Mean Matching
27%
Missing Data
18%
Missing Variables
18%
Joint Distribution
18%
Conditional Model
18%
Potential Scenarios
9%
Regression Analysis
9%
Sensitivity Analysis
9%
Normal Linear Model
9%
Combined Method
9%
Informative Prior
9%
Multiple Imputation
9%
Prior Distribution
9%
Partially Observed
9%
Multivariate Model
9%
Partial Correlation
9%
Treatment Conditions
9%
CA Model
9%
Diagnostic Process
9%
Imputed Data
9%
Polynomial Combination
9%
Missing Data Problem
9%
Specification Method
9%
Inverse Wishart Prior
9%
Model Design
9%
Multivariate Distribution
9%
Conditional Distribution
9%
Posterior Predictive Checks
9%
Gamma Prior
9%
Inverse gamma
9%
Predictive Posterior Distribution
9%
Substantive Model Compatible
9%
Individualized Treatment Effects
9%
Substantive Models
9%
Normal Inverse Wishart
9%
Mathematics
Conditionals
100%
Imputation Method
33%
Joint Modeling
33%
Conditional Model
16%
Joint Distribution
16%
Observed Data
16%
Linear Models
8%
Polynomial
8%
Multiple Imputation
8%
Apply It
8%
Treatment Effect
8%
Informative Prior
8%
Conditional Distribution
8%
Posterior Predictive Distribution
8%
Model Design
8%
Multivariate Distribution
8%
Canonical Regression
8%