We investigate a longitudinal data model with non-parametric regression functions that may vary across the observed individuals. In a variety of applications, it is natural to impose a group structure on the regression curves. Specifically, we may suppose that the observed individuals can be grouped into a number of classes whose members all share the same regression function. We develop a statistical procedure to estimate the unknown group structure from the data. Moreover, we derive the asymptotic properties of the procedure and investigate its finite sample performance by means of a simulation study and a real data example.
Authors
![Person graphic](/sites/default/files/styles/square_desktop/public/2022-06/IFS-person-graphic.png?itok=hWCtTSrz)
Oliver Linton
![Person graphic](/sites/default/files/styles/square_desktop/public/2022-06/IFS-person-graphic.png?itok=hWCtTSrz)
Michael Vogt
Journal article details
- DOI
- 10.1111/rssb.12155
- Publisher
- Wiley
- Issue
- Volume 79, Issue 1, February 2016, pages 5-27
Suggested citation
Linton, O and Vogt, M. (2016). 'Classification of non-parametric regression functions in longitudinal data models' 79(1/2016), pp.5–27.
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