Classification of non-parametric regression functions in longitudinal data models

Michael Vogt, Oliver Linton

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34 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)5-27
Number of pages23
JournalJournal of the Royal Statistical Society. Series B: Statistical Methodology
Volume79
Issue number1
DOIs
Publication statusPublished - 1 Jan 2017
Externally publishedYes

Keywords

  • Classification of regression curves
  • Kernel estimation
  • Longitudinal or panel data
  • Non-parametric regression

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