Abstract
Functional responses are encountered when units are observed over time. Although the whole function itself is not observed, a sufficiently large number of evaluations, as is common with modern recording equipment, are assumed to be available. Functional regression analysis relates the smooth functional response, y(t), to known covariates, x, by a linear combination of parameter functions, β(t), which are to be estimated. The model takes the standard form, y(t) = xTβ(t) + ∊(t). This approach provides an alternative to standard longitudinal data methods used in the biological sciences, where less and noisier data necessitate parametric modeling. The methodology is illustrated by an application in ergonomics.
| Original language | English |
|---|---|
| Pages (from-to) | 254-261 |
| Number of pages | 8 |
| Journal | Technometrics |
| Volume | 39 |
| Issue number | 3 |
| DOIs | |
| Publication status | Published - 1 Jan 1997 |
Funding
I acknowledge the support of Chrysler Corporation, Deborah Thompson in particular, and Donald Chaffin, Ulrich Raschke, and Xudong Zhang at the Center for Ergonomics at the University of Michigan for providing the data and motivation for this article. The article was also improved by the comments and suggestionso f the referees. I carried out this work while visiting the Statistics Group at the University of Bath.
Keywords
- Curve estimation
- Ergonomics
- Functional data analysis
- Longitudinal data analysis
- Nonparametric regression
- Repeated measures
ASJC Scopus subject areas
- Statistics and Probability
- Modelling and Simulation
- Applied Mathematics