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Classification of non-parametric regression functions in longitudinal data models

Accepted version
Peer-reviewed

Type

Article

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Authors

Vogt, M 

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

Volume Title

79

Publisher

Oxford University Press (OUP)