Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/189697 
Year of Publication: 
2018
Series/Report no.: 
cemmap working paper No. CWP08/18
Publisher: 
Centre for Microdata Methods and Practice (cemmap), London
Abstract: 
We study a longitudinal data model with nonparametric regression functions that may vary across the observed subjects. In a wide range of applications, it is natural to assume that not every subject has a completely different regression function. We may rather suppose that the observed subjects can be grouped into a small number of classes whose members share the same regression curve. We develop a bandwidth-free clustering method to estimate the unknown group structure from the data. More specifically, we construct estimators of the un- known classes and their unknown number which are free of classical bandwidth or smoothing parameters. In the theoretical part of the paper, we analyze the statistical properties of our estimators. The technical analysis is complemented by a simulation study and an application to temperature anomaly data.
Subjects: 
Clustering of nonparametric curves
nonparametric regression
multiscalestatistics
longitudinal/panel data
Persistent Identifier of the first edition: 
Document Type: 
Working Paper

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