Classification of non‐parametric regression functions in longitudinal data models | Zendy

Vogt Michael | Zendy; Linton Oliver | Zendy

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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.

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