Nonparametric Sample Selection Models

Nonparametric Sample Selection Models is a sample selection model that belongs to the class of statistical methods used to correct selection bias, distinguished by its avoidance of parametric assumptions about the functional form or distribution of the error terms, thus allowing for more flexible and data-driven estimation of selection mechanisms.

18

Publications

1K

Citations

29

Authors

29

Institutions

Publications per year

2017–2026

1

Variants

Non-parametric Sample Selection Models

Authors

29

  1. WK
    Whitney K. Newey

    Massachusetts Institute of Technology, United States

    2 publications · 446 citations

    2

    h-index

  2. DW
    Donald W. K. Andrews

    Yale University, United States

    2 publications · 249 citations

    2

    h-index

  3. MD
    Mitali Das

    International Monetary Fund, United States

    1 publications · 407 citations

    1

    h-index

  4. SC
    Songnian Chen

    Hong Kong University of Science and Technology, Hong Kong SAR China

    1 publications · 33 citations

    1

    h-index

  5. NB
    N. Balakrishnan

    McMaster University, Canada

    1 publications · 10 citations

    1

    h-index

1–5 of 29

Venues

  1. Journal of Econometrics

    3 publications · 101 citations

    3

    h-index

  2. The Review of Economic Studies

    2 publications · 641 citations

    2

    h-index

  3. RePEc: Research Papers in Economics

    Repository · 1 publications · 15 citations

    1

    h-index

  4. PubMed

    Repository · 1 publications · 23 citations

    1

    h-index

  5. Statistics & Probability Letters

    1 publications · 18 citations

    1

    h-index

1–5