Moderate Deviations Analysis

Moderate Deviations Analysis is a theoretical framework within finite blocklength information theory that characterizes the asymptotic behavior of error probabilities in the regime where deviations from capacity scale slower than the inverse square root of block length, bridging large deviations and central limit scaling.

132

Publications

4.2K

Citations

175

Authors

138

Institutions

Publications per year

2017–2026

20

Authors

175

  1. QS
    Qi-Man Shao

    University of Oregon, United States

    10 publications · 220 citations

    10

    h-index

  2. FG
    Fuqing Gao

    Wuhan University, China

    8 publications · 249 citations

    8

    h-index

  3. AG
    Arnaud Guillin

    Université Clermont Auvergne, France

    6 publications · 201 citations

    6

    h-index

  4. HJ
    Hui Jiang

    University of Michigan, United States

    5 publications · 100 citations

    5

    h-index

  5. ML
    Matthias Löwe

    University of Münster, Germany

    4 publications · 113 citations

    4

    h-index

1–5 of 175

Venues

  1. The Annals of Probability

    14 publications · 625 citations

    13

    h-index

  2. Stochastic Processes and their Applications

    9 publications · 227 citations

    9

    h-index

  3. Statistics & Probability Letters

    7 publications · 146 citations

    7

    h-index

  4. The Annals of Statistics

    6 publications · 229 citations

    6

    h-index

  5. Acta Mathematica Sinica English Series

    5 publications · 104 citations

    5

    h-index

1–5