Missing Data Selection Models
Missing Data Selection Models is a statistical framework that belongs to the class of selection models used in the analysis of missing data, distinguished by its explicit modeling of the process through which data are missing as a function of observed and unobserved variables to correct for potential selection bias.
57
Publications
3K
Citations
142
Authors
104
Institutions
Publications per year
2017–2026
8
Variants
Selection Models For Missing Data
Parents
57
1–5 of 57
142
5
h-index
London School of Hygiene & Tropical Medicine, United Kingdom
4 publications · 377 citations
4
h-index
3
h-index
3
h-index
2
h-index
1–5 of 142
104
Harvard University
Cambridge, United States
5 h-index
10 publications
610 citations
The University of Texas at Austin
Austin, United States
4 h-index
4 publications
346 citations
Transnational University Limburg
Diepenbeek, Belgium
3 h-index
6 publications
368 citations
University of Kent
Canterbury, United Kingdom
3 h-index
3 publications
359 citations
Texas A&M University
College Station, United States
2 h-index
2 publications
146 citations
MRC Biostatistics Unit
Cambridge, United Kingdom
2 h-index
4 publications
76 citations
VA Puget Sound Health Care System
Seattle, United States
2 h-index
2 publications
93 citations
KU Leuven
Leuven, Belgium
2 h-index
4 publications
107 citations
1–8 of 104
7 publications · 225 citations
7
h-index
6 publications · 354 citations
6
h-index
3 publications · 176 citations
3
h-index
2 publications · 31 citations
2
h-index
2 publications · 170 citations
2
h-index
1–5