Model-based Estimation

Model-Based Estimation is a statistical inference approach that employs a structured probabilistic or deterministic framework to predict unobserved quantities by leveraging assumed relationships between observed data and underlying processes, thereby distinguishing it from design-based or non-parametric estimation methods.

1.2K

Publications

143.6K

Citations

3.2K

Authors

1.2K

Institutions

Publications per year

2017–2026

340

Authors

3.2K

  1. TH
    Thomas Heldt

    Massachusetts Institute of Technology, United States

    6 publications · 222 citations

    6

    h-index

  2. EM
    E.K. Miller

    Lawrence Livermore National Laboratory, United States

    5 publications · 471 citations

    5

    h-index

  3. CF
    Charles F. Manski

    Northwestern University, United States

    5 publications · 232 citations

    5

    h-index

  4. AL
    Alexander L. Strehl

    Rutgers, The State University of New Jersey, United States

    5 publications · 715 citations

    5

    h-index

  5. GC
    George C. Verghese

    Massachusetts Institute of Technology, United States

    5 publications · 206 citations

    5

    h-index

1–5 of 3.2K

Venues

  1. Journal of the American Statistical Association

    37 publications · 9.6K citations

    27

    h-index

  2. 13

    h-index

  3. PLoS ONE

    12 publications · 401 citations

    12

    h-index

  4. SSRN Electronic Journal

    Repository · 12 publications · 452 citations

    11

    h-index

  5. Journal of Econometrics

    9 publications · 661 citations

    9

    h-index

1–5