Functional Data Interpolation

Functional Data Interpolation is a functional data analysis technique that belongs to the class of methods for reconstructing continuous functions from discrete observations, distinguished by its specific focus on estimating unobserved segments of a functional trajectory while preserving the inherent smoothness and structure of the underlying stochastic process.

11

Publications

316

Citations

37

Authors

26

Institutions

Publications per year

2017–2026

1

Authors

37

  1. AS
    April Sievert

    Indiana University Bloomington, United States

    2 publications · 86 citations

    2

    h-index

  2. LR
    Larry R. Kimball

    Appalachian State University, United States

    2 publications · 86 citations

    2

    h-index

  3. MA
    Mark Aldenderfer

    University of California, Merced, United States

    2 publications · 86 citations

    2

    h-index

  4. JC
    Jef Caers

    Stanford University, United States

    1 publications · 25 citations

    1

    h-index

  5. IT
    Isneri Talavera

    Advanced Technologies Application Center, Cuba

    1 publications · 27 citations

    1

    h-index

1–5 of 37

Venues

  1. Journal of Field Archaeology

    2 publications · 86 citations

    2

    h-index

  2. PubMed

    Repository · 1 publications · 13 citations

    1

    h-index

  3. Analytica Chimica Acta

    1 publications · 27 citations

    1

    h-index

  4. 1

    h-index

  5. Circuits Systems and Signal Processing

    CSSP · 1 publications · 15 citations

    1

    h-index

1–5