Handling Missing Data

Handling Missing Data is a methodological framework within the domain of exponential smoothing that systematically addresses the absence of observations in time series by imputing or adjusting for missing values through adaptive smoothing mechanisms.

1K

Publications

66.6K

Citations

2.7K

Authors

1.1K

Institutions

Publications per year

2017–2026

394

Authors

2.7K

  1. DB
    Donald B. Rubin

    Harvard University, United States

    9 publications · 832 citations

    9

    h-index

  2. PB
    P.M.T. Broersen

    Delft University of Technology, Netherlands

    7 publications · 198 citations

    7

    h-index

  3. JL
    Jian Li

    University of Florida, United States

    7 publications · 379 citations

    7

    h-index

  4. MR
    Mijke Rhemtulla

    University of California, Davis, United States

    6 publications · 570 citations

    6

    h-index

  5. PS
    Petre Stoica

    Uppsala University, Sweden

    6 publications · 447 citations

    6

    h-index

1–5 of 2.7K

Venues

  1. Journal of the American Statistical Association

    41 publications · 3.1K citations

    26

    h-index

  2. Biometrika

    14 publications · 469 citations

    14

    h-index

  3. Computational Statistics & Data Analysis

    CSDA · 18 publications · 1.5K citations

    14

    h-index

  4. arXiv (Cornell University)

    Repository · 14 publications · 1.1K citations

    13

    h-index

  5. Neurocomputing

    11 publications · 805 citations

    11

    h-index

1–5