Functional Covariance Estimation

Functional Covariance Estimation is a statistical method that characterizes the second-order dependence structure of functional time series by estimating the covariance operator between random functions observed over time, distinguishing it from multivariate or scalar covariance estimation through its treatment of infinite-dimensional functional observations and temporal dependencies.

46

Publications

4.6K

Citations

108

Authors

72

Institutions

Publications per year

2017–2026

14

Authors

108

  1. HM
    Hans‐Georg Müller

    University of California, Davis, United States

    3 publications · 1.9K citations

    3

    h-index

  2. VZ
    Vadim Zipunnikov

    Johns Hopkins University, United States

    2 publications · 167 citations

    2

    h-index

  3. SG
    Sonja Greven

    Ludwig-Maximilians-Universität München, Germany

    2 publications · 418 citations

    2

    h-index

  4. JW
    Jane-Ling Wang

    University of California, Davis, United States

    2 publications · 1.6K citations

    2

    h-index

  5. DP
    Debashis Paul

    University of California, Davis, United States

    2 publications · 142 citations

    2

    h-index

1–5 of 108

Venues

  1. Journal of the American Statistical Association

    8 publications · 2.4K citations

    8

    h-index

  2. The Annals of Statistics

    5 publications · 682 citations

    5

    h-index

  3. Biometrika

    3 publications · 205 citations

    3

    h-index

  4. Biometrics

    3 publications · 100 citations

    3

    h-index

  5. Computational Statistics & Data Analysis

    CSDA · 2 publications · 45 citations

    2

    h-index

1–5