Multiscale clustering of nonparametric regression curves

Michael Vogt and Oliver Linton
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Michael Vogt: Institute for Fiscal Studies

No CWP08/18, CeMMAP working papers from Centre for Microdata Methods and Practice, Institute for Fiscal Studies

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 speci cally, we construct estimators of the unknown 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.

Keywords: Clustering of nonparametric curves; nonparametric regression; multiscale statistics; longitudinal/panel data (search for similar items in EconPapers)
Date: 2018-01-10
New Economics Papers: this item is included in nep-ecm
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Citations: View citations in EconPapers (1)

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Journal Article: Multiscale clustering of nonparametric regression curves (2020)
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