Asymptotic Normality

Asymptotic Normality is a theoretical property of an estimator stating that, as the sample size approaches infinity, the distribution of the estimator converges to a normal distribution centered at the true parameter value, with variance decreasing at a rate proportional to the inverse of the sample size.

4.1K

Publications

298.8K

Citations

5K

Authors

1.7K

Institutions

Publications per year

2017–2026

446

Authors

5K

  1. JF
    Jianqing Fan

    Princeton University, United States

    24 publications · 3.9K citations

    21

    h-index

  2. HD
    Holger Dette

    Ruhr University Bochum, Germany

    19 publications · 861 citations

    16

    h-index

  3. HL
    Hira L. Koul

    Michigan State University, United States

    16 publications · 784 citations

    15

    h-index

  4. MH
    Marc Hallin

    Université Libre de Bruxelles, Belgium

    17 publications · 1.1K citations

    15

    h-index

  5. LW
    Lionel Weiss

    Cornell University, United States

    16 publications · 392 citations

    14

    h-index

1–5 of 5K

Venues

  1. The Annals of Statistics

    402 publications · 47.6K citations

    113

    h-index

  2. Journal of the American Statistical Association

    306 publications · 24.9K citations

    80

    h-index

  3. The Annals of Mathematical Statistics

    129 publications · 25K citations

    64

    h-index

  4. Econometrica

    67 publications · 16.1K citations

    52

    h-index

  5. 50

    h-index

1–5