Poisson Process Models is a stochastic counting process model that assumes events occur continuously and independently at a constant average rate, serving as a foundational framework for analyzing recurrent event data in time-to-event analysis.
3.4K
Publications
180.2K
Citations
8K
Authors
2.5K
Institutions
Publications per year
2017–2026
538
Variants
Poisson Process Model
Parents
Publications
3.4K
Model-Based Gaussian and Non-Gaussian Clustering
Jeffrey D. Banfield, Adrian E. Raftery · Biometrics · 1993 · 2.4K citations
A mathematical model of the finding of usability problems
Jakob Nielsen, Thomas K. Landauer · 1993 · 1.9K citations · Full text
Poisson Arrivals See Time Averages
Ronald W. Wolff · Operations Research · 1982 · 1.2K citations
1–5 of 3.4K
Authors
8K
- CW
16
h-index
- HP
12
h-index
- MF
11
h-index
- HS
11
h-index
- ME
10
h-index
1–5 of 8K
Institutions
2.5K
University of California, Berkeley
Berkeley, United States
33 h-index
95 publications
6.8K citations
Stanford University
Stanford, United States
30 h-index
69 publications
4.7K citations
Columbia University
New York, United States
28 h-index
71 publications
3.7K citations
University of Michigan
Ann Arbor, United States
24 h-index
54 publications
3.3K citations
Massachusetts Institute of Technology
Cambridge, United States
24 h-index
45 publications
3.6K citations
Imperial College London
London, United Kingdom
24 h-index
47 publications
4.2K citations
University College London
London, United Kingdom
23 h-index
56 publications
5.3K citations
Virginia Tech
Blacksburg, United States
21 h-index
54 publications
2K citations
1–8 of 2.5K
Venues
- Journal of Applied Probability
JAP · 176 publications · 6.1K citations
40
h-index
- Operations Research
54 publications · 5.5K citations
38
h-index
- Advances in Applied Probability
AAP · 91 publications · 3.4K citations
33
h-index
- Journal of the American Statistical Association
48 publications · 4.3K citations
28
h-index
- Management Science
33 publications · 2.3K citations
25
h-index
1–5