Bootstrap For Functional Data

Bootstrap for Functional Data is a statistical resampling methodology that belongs to the class of nonparametric bootstrap techniques and is specifically distinguished by its adaptation to functional observations, where data are treated as realizations of random curves or functions rather than scalar or vector-valued random variables.

18

Publications

755

Citations

43

Authors

35

Institutions

Publications per year

2017–2026

6

Authors

43

  1. FF
    Frédéric Ferraty

    Université Toulouse III - Paul Sabatier, France

    2 publications · 113 citations

    2

    h-index

  2. PV
    Philippe Vieu

    Université Toulouse III - Paul Sabatier, France

    2 publications · 113 citations

    2

    h-index

  3. OA
    Olga A. Vsevolozhskaya

    University of Kentucky, United States

    1 publications · 11 citations

    1

    h-index

  4. CZ
    Chongqi Zhang

    Guangzhou University, China

    1 publications · 54 citations

    1

    h-index

  5. YS
    Y. Samuel Wang

    University of Washington, United States

    1 publications · 15 citations

    1

    h-index

1–5 of 43

Venues

  1. Journal of Multivariate Analysis

    4 publications · 62 citations

    4

    h-index

  2. Biometrika

    2 publications · 79 citations

    2

    h-index

  3. The Annals of Applied Statistics

    AOAS · 1 publications · 15 citations

    1

    h-index

  4. Statistical Modelling

    1 publications · 13 citations

    1

    h-index

  5. Computational Statistics & Data Analysis

    CSDA · 1 publications · 184 citations

    1

    h-index

1–5