Kernel-based Estimation

Kernel-Based Estimation is a nonparametric statistical method that belongs to the class of smoothing techniques in time series analysis, distinguished by its use of a kernel function to assign weighted averages to nearby observations for estimating underlying structures such as volatility or spectral density in high-frequency data.

1.9K

Publications

134.8K

Citations

3.1K

Authors

1.3K

Institutions

Publications per year

2017–2026

345

Authors

3.1K

  1. MC
    M. C. Jones

    The Open University, United Kingdom

    28 publications · 5.5K citations

    22

    h-index

  2. JS
    J. S. Marron

    University of North Carolina at Chapel Hill, United States

    19 publications · 3.3K citations

    18

    h-index

  3. PH
    Peter Hall

    Australian National University, Australia

    21 publications · 1.8K citations

    18

    h-index

  4. WK
    Wolfgang Karl Härdle

    Humboldt-Universität zu Berlin, Germany

    16 publications · 1.5K citations

    16

    h-index

  5. JS
    Jeffrey S. Racine

    McMaster University, Canada

    12 publications · 1.7K citations

    12

    h-index

1–5 of 3.1K

Venues

  1. The Annals of Statistics

    108 publications · 16.4K citations

    66

    h-index

  2. Journal of the American Statistical Association

    62 publications · 5.2K citations

    35

    h-index

  3. Journal of nonparametric statistics

    84 publications · 2.8K citations

    33

    h-index

  4. Econometric Theory

    44 publications · 3.7K citations

    28

    h-index

  5. Biometrika

    34 publications · 4.2K citations

    25

    h-index

1–5