Missing Data Propensity Modeling
Missing Data Propensity Modeling is a statistical methodology that belongs to the class of propensity modeling techniques, specifically designed to estimate the probability of data missingness mechanisms in observed datasets, thereby enabling the identification and adjustment for systematic biases due to incomplete data.
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989
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2017–2026
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Semiparametric inverse propensity weighting for nonignorable missing data
Jun Shao, Lei Wang · Biometrika · 2016 · 139 citations
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University of Bamberg
Bamberg, Germany
2 h-index
3 publications
201 citations

University of Tübingen
Tübingen, Germany
2 h-index
2 publications
138 citations
Université de Montréal
Montreal, Canada
1 h-index
4 publications
160 citations
Urban Institute
Washington D.C., United States
1 h-index
1 publications
72 citations
Cancer Research And Biostatistics
Seattle, United States
1 h-index
1 publications
79 citations

University of Göttingen
Göttingen, Germany
1 h-index
1 publications
17 citations
Philipps University of Marburg
Marburg, Germany
1 h-index
1 publications
17 citations

Johns Hopkins University
Baltimore, United States
1 h-index
2 publications
26 citations
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2 publications · 161 citations
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JBES · 2 publications · 86 citations
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Conference · 1 publications · 23 citations
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1 publications · 13 citations
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1 publications · 79 citations
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