Dynamic Generalized Linear Models

Dynamic Generalized Linear Models is a class of state-space models that extends generalized linear models by allowing the regression coefficients to evolve dynamically over time according to a specified stochastic process, thereby enabling flexible modeling of time-dependent observational data with non-Gaussian distributions.

75

Publications

6K

Citations

180

Authors

120

Institutions

Publications per year

2017–2026

18

Authors

180

  1. MD
    Manfred Deistler

    TU Wien, Austria

    4 publications · 178 citations

    4

    h-index

  2. HS
    Hélio S. Migon

    Universidade Federal do Rio de Janeiro, Brazil

    3 publications · 629 citations

    3

    h-index

  3. GB
    George B. Arhonditsis

    University of Toronto, Canada

    3 publications · 80 citations

    3

    h-index

  4. MW
    Mike West

    Duke University, United States

    3 publications · 561 citations

    3

    h-index

  5. SP
    Satyendra P. Bhavsar

    Ministry of the Environment, Conservation and Parks, Canada

    3 publications · 80 citations

    3

    h-index

1–5 of 180

Venues

  1. 6

    h-index

  2. International Statistical Review

    2 publications · 31 citations

    2

    h-index

  3. Communications in Information and Systems

    2 publications · 34 citations

    2

    h-index

  4. South African Journal of Economic and Management Sciences

    SAJEMS · 2 publications · 59 citations

    2

    h-index

  5. Methods in Ecology and Evolution

    1 publications · 51 citations

    1

    h-index

1–5