Bayesian Missing Data Analysis
Bayesian Missing Data Analysis is a statistical methodology that belongs to the class of Bayesian inference techniques and is distinguished by its formal integration of missing data mechanisms into the probabilistic model through the specification of prior distributions and joint modeling of observed and missing values.
220
Publications
28K
Citations
544
Authors
279
Institutions
Publications per year
2017–2026
45
Parents
220
1–5 of 220
544
University of North Carolina at Chapel Hill, United States
8 publications · 240 citations
8
h-index
6
h-index
5
h-index
5
h-index
5
h-index
1–5 of 544
279
Chinese University of Hong Kong
Hong Kong, Hong Kong
10 h-index
17 publications
636 citations
University of Michigan
Ann Arbor, United States
9 h-index
22 publications
749 citations
Duke University
Durham, United States
7 h-index
17 publications
787 citations
University of North Carolina at Chapel Hill
Chapel Hill, United States
6 h-index
13 publications
210 citations
University of Cambridge
Cambridge, United Kingdom
6 h-index
8 publications
542 citations
University of Connecticut
Storrs, United States
6 h-index
6 publications
1.3K citations
Carnegie Mellon University
Pittsburgh, United States
5 h-index
5 publications
148 citations
Harvard University
Cambridge, United States
5 h-index
7 publications
1.4K citations
1–8 of 279
17 publications · 2.8K citations
13
h-index
16 publications · 537 citations
12
h-index
7 publications · 431 citations
7
h-index
7 publications · 11.6K citations
7
h-index
5 publications · 600 citations
5
h-index
1–5