Classification of non-parametric regression functions in longitudinal data models
Journal of the Royal Statistical Society. Series B: Statistical Methodology
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Vogt, M., & Linton, O. (2017). Classification of non-parametric regression functions in longitudinal data models. Journal of the Royal Statistical Society. Series B: Statistical Methodology, 79 (1), 5-27. https://doi.org/10.1111/rssb.12155
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.
External DOI: https://doi.org/10.1111/rssb.12155
This record's URL: https://www.repository.cam.ac.uk/handle/1810/289338