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

Authors

42

  1. MM
    Micol Marchetti-Bowick

    Carnegie Mellon University, United States

    1 publications · 21 citations

    1

    h-index

  2. EP
    Eric P. Xing

    Carnegie Mellon University, United States

    1 publications · 21 citations

    1

    h-index

  3. YR
    Yogesh Rathi

    Harvard University, United States

    1 publications · 14 citations

    1

    h-index

  4. JD
    Jon D. Hennebold

    Oregon National Primate Research Center, United States

    1 publications · 27 citations

    1

    h-index

  5. MR
    Matthew Reimherr

    Pennsylvania State University, United States

    1 publications · 22 citations

    1

    h-index

1–5 of 42

Venues

  1. Technometrics

    2 publications · 43 citations

    2

    h-index

  2. Bioinformatics

    1 publications · 21 citations

    1

    h-index

  3. Journal of Hydrology

    1 publications · 45 citations

    1

    h-index

  4. 1

    h-index

  5. Microvascular Research

    MVR · 1 publications · 41 citations

    1

    h-index

1–5