Robust Density Estimation

Robust Density Estimation is a density estimation method that maintains accurate and stable performance in the presence of outliers, model misspecification, or deviations from underlying distributional assumptions, thereby distinguishing itself from conventional density estimation techniques by its resilience to data anomalies and modeling uncertainties.

84

Publications

4.4K

Citations

1.5K

Authors

320

Institutions

Publications per year

2017–2026

22

Authors

1.5K

  1. CS
    Clayton Scott

    University of Michigan, United States

    4 publications · 448 citations

    4

    h-index

  2. AB
    Ayanendranath Basu

    Indian Statistical Institute, India

    3 publications · 851 citations

    3

    h-index

  3. JK
    JooSeuk Kim

    University of Michigan, United States

    3 publications · 434 citations

    3

    h-index

  4. PJ
    Peter J. Diggle

    Lancaster University, United Kingdom

    3 publications · 128 citations

    3

    h-index

  5. CA
    Claudio Agostinelli

    Ca' Foscari University of Venice, Italy

    3 publications · 85 citations

    3

    h-index

1–5 of 1.5K

Venues

  1. Biometrika

    6 publications · 971 citations

    6

    h-index

  2. arXiv (Cornell University)

    Repository · 5 publications · 273 citations

    5

    h-index

  3. The Annals of Statistics

    5 publications · 363 citations

    5

    h-index

  4. IEEE Transactions on Information Theory

    4 publications · 187 citations

    4

    h-index

  5. Journal of Statistical Computation and Simulation

    JSCS · 4 publications · 71 citations

    4

    h-index

1–5