Bootstrap Risk Estimation

Bootstrap Risk Estimation is a resampling-based statistical method that belongs to the class of nonparametric risk estimation techniques, distinguished by its use of repeated sampling with replacement from the observed data to approximate the sampling distribution of a risk estimator and thereby assess its uncertainty or variability.

20

Publications

444

Citations

41

Authors

30

Institutions

Publications per year

2017–2026

0

Authors

41

  1. SM
    Seyed Meysam Mousavi

    Shahed University, Iran

    2 publications · 61 citations

    2

    h-index

  2. RV
    Richard Verrall

    City, University of London, United Kingdom

    2 publications · 50 citations

    2

    h-index

  3. JH
    Joseph Hyun Tae Kim

    University of Waterloo, Canada

    2 publications · 33 citations

    2

    h-index

  4. MM
    Mohammad Mojtahedi

    UNSW Sydney, Australia

    2 publications · 61 citations

    2

    h-index

  5. MR
    Mary R. Hardy

    University of Waterloo, Canada

    2 publications · 33 citations

    2

    h-index

1–5 of 41

Venues

  1. Astin Bulletin

    3 publications · 44 citations

    3

    h-index

  2. Insurance Mathematics and Economics

    1 publications · 17 citations

    1

    h-index

  3. The Journal of Risk Model Validation

    1 publications · 24 citations

    1

    h-index

  4. Applied Mathematical Finance

    1 publications · 12 citations

    1

    h-index

  5. Data Archiving and Networked Services (DANS)

    Repository · 1 publications · 15 citations

    1

    h-index

1–5