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Vogt, M. and Linton, O.

Classification of Non-Parametric Regression Functions in Longitudinal Data Models

Journal of the Royal Statistical Society. Series B: Statistical Methodology

Vol. 79(1) pp. 5-27 (2017)

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.

Keywords: Classification of regression curves, Kernel estimation, Longitudinal or panel data, Non-parametric regression

Author links: Oliver Linton  

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Theme: empirical