Monte Carlo Estimation

Monte Carlo Estimation is a parameter estimation method that belongs to the class of numerical techniques relying on random sampling, distinguished by its use of repeated stochastic simulations to approximate unknown parameters through the law of large numbers.

3.4K

Publications

324.4K

Citations

6.4K

Authors

2.1K

Institutions

Publications per year

2017–2026

468

Authors

6.4K

  1. PW
    Peter W. Glynn

    Stanford University, United States

    21 publications · 2.5K citations

    17

    h-index

  2. PG
    Paul Glasserman

    Columbia University, United States

    14 publications · 1.8K citations

    14

    h-index

  3. PL
    Pierre L’Ecuyer

    Université de Montréal, Canada

    17 publications · 577 citations

    14

    h-index

  4. GS
    George S. Fishman

    University of North Carolina at Chapel Hill, United States

    13 publications · 865 citations

    12

    h-index

  5. AJ
    Ajay Jasra

    National University of Singapore, Singapore

    13 publications · 449 citations

    12

    h-index

1–5 of 6.4K

Venues

  1. Journal of the American Statistical Association

    139 publications · 38.3K citations

    58

    h-index

  2. Technometrics

    75 publications · 33.2K citations

    37

    h-index

  3. Biometrics

    39 publications · 3.3K citations

    28

    h-index

  4. Biometrika

    41 publications · 22.9K citations

    27

    h-index

  5. arXiv (Cornell University)

    Repository · 65 publications · 2.7K citations

    27

    h-index

1–5