Classification of non-parametric regression functions in longitudinal data models
Accepted version
Peer-reviewed
Repository URI
Repository DOI
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Authors
Vogt, M
Linton, Oliver https://orcid.org/0000-0003-2313-0564
Abstract
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.
Description
Keywords
49 Mathematical Sciences, 38 Economics, 4905 Statistics, 3802 Econometrics
Journal Title
Journal of the Royal Statistical Society. Series B: Statistical Methodology
Conference Name
Journal ISSN
1369-7412
1467-9868
1467-9868
Volume Title
79
Publisher
Oxford University Press (OUP)