|Title||Longitudinal dynamic functional regression.|
|Publication Type||Journal Article|
|Year of Publication||2020|
|Authors||Staicu, Ana-Maria, Md Nazmul Islam, Raluca Dumitru, and Eric van Heugten|
|Journal||J R Stat Soc Ser C Appl Stat|
|Date Published||2020 Jan|
The paper develops a parsimonious modelling framework to study the time-varying association between scalar outcomes and functional predictors observed at many instances, in longitudinal studies. The methods enable us to reconstruct the full trajectory of the response and are applicable to Gaussian and non-Gaussian responses. The idea is to model the time-varying functional predictors by using orthogonal basis functions and to expand the time-varying regression coefficient by using the same basis. Numerical investigation through simulation studies and data analysis show excellent performance in terms of accurate prediction and efficient computations, when compared with existing alternatives. The methods are inspired and applied to an animal science application, where of interest is to study the association between the feed intake of lactating sows and the minute-by-minute temperature throughout the 21 days of their lactation period. R code and an R illustration are provided.
|Alternate Journal||J R Stat Soc Ser C Appl Stat|
|Original Publication||Longitudinal dynamic functional regression.|
|PubMed Central ID||PMC6953745|
|Grant List||P01 CA142538 / CA / NCI NIH HHS / United States |
R01 NS085211 / NS / NINDS NIH HHS / United States
Longitudinal dynamic functional regression.