Irregular Sampling Models

Irregular Sampling Models is a statistical modeling framework designed to analyze time series data characterized by non-uniform observation intervals, distinguishing it from regular time series models by explicitly accounting for the variable temporal spacing between successive measurements.

94

Publications

5K

Citations

194

Authors

135

Institutions

Publications per year

2017–2026

18

Variants

Irregularly Spaced Time Series Models

Authors

194

  1. KG
    Karlheinz Gröchenig

    University of Vienna, Austria

    6 publications · 798 citations

    6

    h-index

  2. HG
    Hans G. Feichtinger

    University of Vienna, Austria

    4 publications · 172 citations

    4

    h-index

  3. ÉR
    Éric Renault

    University of North Carolina at Chapel Hill, United States

    2 publications · 43 citations

    2

    h-index

  4. RF
    Robert F. Engle

    New York University, United States

    2 publications · 255 citations

    2

    h-index

  5. JR
    Jeffrey R. Russell

    University of Chicago, United States

    2 publications · 255 citations

    2

    h-index

1–5 of 194

Venues

  1. The Astrophysical Journal

    ApJ · 4 publications · 437 citations

    4

    h-index

  2. Mathematics of Computation

    MCOM · 3 publications · 462 citations

    3

    h-index

  3. arXiv (Cornell University)

    Repository · 3 publications · 127 citations

    3

    h-index

  4. IEEE Transactions on Information Theory

    3 publications · 134 citations

    3

    h-index

  5. IEEE Transactions on Signal Processing

    3 publications · 212 citations

    3

    h-index

1–5