Gaussian Process Imputation

Gaussian Process Imputation is a probabilistic imputation method that models the underlying data distribution as a Gaussian process, leveraging covariance structures to infer missing values based on observed data.

11

Publications

742

Citations

33

Authors

19

Institutions

Publications per year

2017–2026

8

Variants

Gp Imputation

Authors

33

  1. HY
    Hui Yang

    Pennsylvania State University, United States

    2 publications · 38 citations

    2

    h-index

  2. FI
    Farhad Imani

    Pennsylvania State University, United States

    2 publications · 38 citations

    2

    h-index

  3. CC
    Changqing Cheng

    Binghamton University, United States

    2 publications · 38 citations

    2

    h-index

  4. RC
    Ruimin Chen

    Pennsylvania State University, United States

    2 publications · 38 citations

    2

    h-index

  5. AL
    Alkes L. Price

    Harvard University, United States

    1 publications · 231 citations

    1

    h-index

1–5 of 33

Venues

  1. Journal of the American Medical Informatics Association

    JAMIA · 2 publications · 134 citations

    2

    h-index

  2. Bioinformatics

    1 publications · 231 citations

    1

    h-index

  3. Applied Soft Computing

    1 publications · 123 citations

    1

    h-index

  4. Technometrics

    1 publications · 25 citations

    1

    h-index

  5. Journal of Computational and Graphical Statistics

    JCGS · 1 publications · 44 citations

    1

    h-index

1–5