Forecasting Under Missing Data

Forecasting Under Missing Data is a forecasting framework that specifically addresses the challenge of predicting future outcomes in immunization programs when historical or real-time data are incomplete or absent, by incorporating methods to estimate, impute, or account for missing observations in the predictive modeling process.

62

Publications

2.8K

Citations

209

Authors

101

Institutions

Publications per year

2017–2026

46

Authors

209

  1. CC
    Charu C. Aggarwal

    IBM (United States), United States

    2 publications · 133 citations

    2

    h-index

  2. YG
    Yagmur Gizem Cinar

    Université Grenoble Alpes, France

    2 publications · 110 citations

    2

    h-index

  3. SW
    Suhang Wang

    Pennsylvania State University, United States

    2 publications · 133 citations

    2

    h-index

  4. HA
    Hyun Ahn

    Kyonggi University, South Korea

    2 publications · 105 citations

    2

    h-index

  5. HY
    Huaxiu Yao

    Pennsylvania State University, United States

    2 publications · 133 citations

    2

    h-index

1–5 of 209

Venues

  1. arXiv (Cornell University)

    Repository · 3 publications · 90 citations

    3

    h-index

  2. Journal of Computer Applications

    2 publications · 46 citations

    2

    h-index

  3. IEEE Access

    2 publications · 70 citations

    2

    h-index

  4. Journal of Forecasting

    2 publications · 124 citations

    2

    h-index

  5. Pattern Recognition Letters

    1 publications · 82 citations

    1

    h-index

1–5