Empirical Process Large Deviations

Empirical Process Large Deviations is a theoretical framework within large deviation theory that characterizes the asymptotic exponential decay rates of probabilities associated with deviations of empirical processes from their typical behavior, distinguished by its focus on path-space large deviations for sequences of stochastic processes indexed by function classes.

36

Publications

1.7K

Citations

46

Authors

32

Institutions

Publications per year

2017–2026

1

Authors

46

  1. FD
    Frank den Hollander

    Leiden University, Netherlands

    3 publications · 79 citations

    3

    h-index

  2. TI
    Tadeusz Inglot

    Wrocław University of Science and Technology, Poland

    2 publications · 38 citations

    2

    h-index

  3. LH
    Lajos Horváth

    University of Utah, United States

    2 publications · 64 citations

    2

    h-index

  4. TL
    Teresa Ledwina

    Polish Academy of Sciences, Poland

    2 publications · 38 citations

    2

    h-index

  5. AG
    Andreas Greven

    Friedrich-Alexander-Universität Erlangen-Nürnberg, Germany

    2 publications · 51 citations

    2

    h-index

1–5 of 46

Venues

  1. The Annals of Probability

    12 publications · 601 citations

    11

    h-index

  2. Stochastic Processes and their Applications

    7 publications · 203 citations

    7

    h-index

  3. Probability Theory and Related Fields

    3 publications · 134 citations

    3

    h-index

  4. Comptes Rendus Mathématique

    1 publications · 277 citations

    1

    h-index

  5. The Annals of Statistics

    1 publications · 25 citations

    1

    h-index

1–5