Sparse Parameter Estimation

Sparse Parameter Estimation is a parameter estimation method that specifically seeks to identify a minimal subset of nonzero parameters within an underdetermined or noisy system by enforcing sparsity-promoting constraints or priors, distinguishing it from general estimation techniques through its focus on parsimonious model representation.

661

Publications

55.3K

Citations

1.4K

Authors

584

Institutions

Publications per year

2017–2026

235

Authors

1.4K

  1. VC
    Victor Chernozhukov

    Massachusetts Institute of Technology, United States

    16 publications · 2.1K citations

    16

    h-index

  2. PS
    Petre Stoica

    Uppsala University, Sweden

    13 publications · 1.9K citations

    12

    h-index

  3. AB
    Alexandre Belloni

    Duke University, United States

    10 publications · 1.7K citations

    10

    h-index

  4. JL
    Jian Li

    University of Florida, United States

    9 publications · 1.2K citations

    9

    h-index

  5. SC
    Sheng Chen

    University of Southampton, United Kingdom

    7 publications · 164 citations

    7

    h-index

1–5 of 1.4K

Venues

  1. IEEE Transactions on Signal Processing

    40 publications · 3.5K citations

    26

    h-index

  2. The Annals of Statistics

    29 publications · 3.9K citations

    26

    h-index

  3. arXiv (Cornell University)

    Repository · 57 publications · 2.3K citations

    26

    h-index

  4. Signal Processing

    19 publications · 808 citations

    14

    h-index

  5. Computational Statistics & Data Analysis

    CSDA · 14 publications · 418 citations

    12

    h-index

1–5