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.

15

Publications

989

Citations

55

Authors

42

Institutions

Publications per year

2017–2026

3

Authors

55

  1. RJ
    Roderick J. A. Little

    University of Michigan, United States

    3 publications · 165 citations

    3

    h-index

  2. SP
    Steffi Pohl

    Freie Universität Berlin, Germany

    2 publications · 161 citations

    2

    h-index

  3. SM
    Sharon M. Hirabayashi

    Mathematica Policy Research, United States

    2 publications · 86 citations

    2

    h-index

  4. DB
    Donald B. Rubin

    Harvard University, United States

    2 publications · 86 citations

    2

    h-index

  5. JL
    John L. Czajka

    Urban Institute, United States

    2 publications · 86 citations

    2

    h-index

1–5 of 55

Venues

  1. Educational and Psychological Measurement

    2 publications · 161 citations

    2

    h-index

  2. Journal of Business and Economic Statistics

    JBES · 2 publications · 86 citations

    2

    h-index

  3. Proceedings of the AAAI Conference on Artificial Intelligence

    Conference · 1 publications · 23 citations

    1

    h-index

  4. Psychometrika

    1 publications · 13 citations

    1

    h-index

  5. Statistical Science

    1 publications · 79 citations

    1

    h-index

1–5