Missing Data Model Selection
Missing Data Model Selection is a model selection framework that specifically addresses the challenge of choosing optimal statistical models for categorical data in the presence of missing values by incorporating mechanisms to account for missingness patterns and their impact on model performance.
151
Publications
18.2K
Citations
467
Authors
275
Institutions
Publications per year
2017–2026
52
151
1–5 of 151
467
5
h-index
4
h-index
3
h-index
London School of Hygiene & Tropical Medicine, United Kingdom
3 publications · 79 citations
3
h-index
University of North Carolina at Chapel Hill, United States
3 publications · 250 citations
3
h-index
1–5 of 467
275
University of California, Davis
Davis, United States
7 h-index
17 publications
715 citations
University of Pennsylvania
Philadelphia, United States
6 h-index
9 publications
255 citations
University of Wisconsin–Madison
Madison, United States
5 h-index
5 publications
308 citations
MRC Biostatistics Unit
Cambridge, United Kingdom
4 h-index
7 publications
9.8K citations
Harvard University
Cambridge, United States
4 h-index
5 publications
66 citations

Hasselt University
Hasselt, Belgium
4 h-index
9 publications
323 citations
University of California, Los Angeles
Los Angeles, United States
3 h-index
5 publications
1.5K citations

Pennsylvania State University
State College, United States
3 h-index
5 publications
1.1K citations
1–8 of 275
11 publications · 1.7K citations
11
h-index
CSDA · 9 publications · 306 citations
9
h-index
9 publications · 9.9K citations
9
h-index
6 publications · 243 citations
6
h-index
3 publications · 72 citations
3
h-index
1–5