Zero-inflated Regression

Zero-Inflated Regression is a statistical modeling framework that belongs to the class of regression models designed for count data, distinguished by its incorporation of a dual-process mechanism that separately models structural zeros and sampling zeros to account for excess zero observations beyond what standard count distributions would predict.

661

Publications

38.7K

Citations

2.3K

Authors

1.1K

Institutions

Publications per year

2017–2026

281

Parents

Authors

2.3K

  1. AH
    Andy H. Lee

    Curtin University, Australia

    11 publications · 1.1K citations

    11

    h-index

  2. KK
    Kelvin K.W. Yau

    City University of Hong Kong, Hong Kong SAR China

    10 publications · 1.1K citations

    10

    h-index

  3. JS
    John S. Preisser

    University of North Carolina at Chapel Hill, United States

    7 publications · 375 citations

    7

    h-index

  4. DB
    Daniel B. Hall

    University of Georgia, United States

    6 publications · 1.2K citations

    6

    h-index

  5. JW
    James W. Hardin

    University of South Carolina, United States

    6 publications · 181 citations

    6

    h-index

1–5 of 2.3K

Venues

  1. PLoS ONE

    24 publications · 946 citations

    19

    h-index

  2. Statistics in Medicine

    17 publications · 779 citations

    14

    h-index

  3. 13

    h-index

  4. Computational Statistics & Data Analysis

    CSDA · 12 publications · 1.1K citations

    11

    h-index

  5. Journal of Applied Statistics

    15 publications · 392 citations

    11

    h-index

1–5