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
Variants
Dglm
75
1–5 of 75
180
4
h-index
3
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Ministry of the Environment, Conservation and Parks, Canada
3 publications · 80 citations
3
h-index
1–5 of 180
120

TU Wien
Vienna, Austria
4 h-index
6 publications
273 citations

University of Toronto
Toronto, Canada
4 h-index
13 publications
2.3K citations

Australian National University
Canberra, Australia
3 h-index
5 publications
833 citations

Universidade Federal do Rio de Janeiro
Rio de Janeiro, Brazil
3 h-index
3 publications
754 citations
LinkedIn (United States)
Sunnyvale, United States
2 h-index
2 publications
24 citations
University of Sheffield
Sheffield, United Kingdom
2 h-index
5 publications
188 citations

NSF National Center for Atmospheric Research
Boulder, United States
2 h-index
2 publications
98 citations
Asian Development Bank
Manila, Philippines
2 h-index
2 publications
219 citations
1–8 of 120
6 publications · 933 citations
6
h-index
2 publications · 31 citations
2
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2 publications · 34 citations
2
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SAJEMS · 2 publications · 59 citations
2
h-index
1 publications · 51 citations
1
h-index
1–5