Functional Data Imputation

Functional Data Imputation is a statistical methodology within functional data analysis that specifically addresses the estimation of missing or unobserved segments in functional time series by leveraging the inherent smoothness, temporal dependence, and global structure of the data.

16

Publications

1.1K

Citations

49

Authors

35

Institutions

Publications per year

2017–2026

12

Authors

49

  1. HL
    Hui Li

    Harbin Institute of Technology, China

    3 publications · 156 citations

    3

    h-index

  2. YB
    Yuequan Bao

    Harbin Institute of Technology, China

    3 publications · 156 citations

    3

    h-index

  3. ZC
    Zhicheng Chen

    Harbin Institute of Technology, China

    3 publications · 156 citations

    3

    h-index

  4. HJ
    Hani Jieun Kim

    The University of Sydney, Australia

    2 publications · 135 citations

    2

    h-index

  5. DE
    David E. James

    Garvan Institute of Medical Research, Australia

    2 publications · 135 citations

    2

    h-index

1–5 of 49

Venues

  1. 3

    h-index

  2. Structural Health Monitoring

    SHM · 3 publications · 156 citations

    3

    h-index

  3. Computational Statistics & Data Analysis

    CSDA · 1 publications · 25 citations

    1

    h-index

  4. Ecological Informatics

    1 publications · 47 citations

    1

    h-index

  5. Statistics & Probability Letters

    1 publications · 20 citations

    1

    h-index

1–5