Dynamic Functional Regression
Dynamic Functional Regression is a functional regression framework that models the relationship between time-dependent functional responses and predictors by incorporating temporal dynamics through lagged effects and functional coefficients, distinguishing it from static functional regression models by explicitly accounting for the evolution of functional relationships over time.
12
Publications
470
Citations
42
Authors
27
Institutions
Publications per year
2017–2026
5
12
Sinem Burcu Erdoğan, Yunjie Tong, Lia M. Hocke et al. · Frontiers in Human Neuroscience · 2016 · 104 citations · Full text
Streamflow forecasting using functional regression
Pierre Masselot, Sophie Dabo‐Niang, Fateh Chebana et al. · Journal of Hydrology · 2016 · 45 citations · Full text
Dynamic Multivariate Functional Data Modeling via Sparse Subspace Learning
Chen Zhang, Hao Yan, Seungho Lee et al. · Technometrics · 2020 · 30 citations
1–5 of 12
42
1
h-index
1
h-index
1
h-index
1
h-index
1
h-index
1–5 of 42
27
Harvard University
Cambridge, United States
2 h-index
6 publications
444 citations
North Carolina State University
Raleigh, United States
2 h-index
4 publications
68 citations

Pennsylvania State University
State College, United States
1 h-index
1 publications
22 citations
Oregon National Primate Research Center
Beaverton, United States
1 h-index
3 publications
81 citations

Brigham and Women's Hospital
Boston, United States
1 h-index
2 publications
28 citations

University of Calgary
Calgary, Canada
1 h-index
1 publications
104 citations

Ruijin Hospital
Shanghai, China
1 h-index
4 publications
164 citations
University of Arizona
Tucson, United States
1 h-index
1 publications
21 citations
1–8 of 27
2 publications · 43 citations
2
h-index
1 publications · 21 citations
1
h-index
1 publications · 45 citations
1
h-index
1 publications · 14 citations
1
h-index
MVR · 1 publications · 41 citations
1
h-index
1–5