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Classification of non‐parametric regression functions in longitudinal data models
Author(s) -
Vogt Michael,
Linton Oliver
Publication year - 2017
Publication title -
journal of the royal statistical society: series b (statistical methodology)
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 6.523
H-Index - 137
eISSN - 1467-9868
pISSN - 1369-7412
DOI - 10.1111/rssb.12155
Subject(s) - regression , regression analysis , parametric statistics , statistics , cross sectional regression , longitudinal data , regression diagnostic , nonparametric regression , mathematics , sample (material) , computer science , econometrics , data mining , polynomial regression , chemistry , chromatography
Summary 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.