Cluster analysis of longitudinal profiles with subgroups
type: Item
instance of: scholarly article
title: Cluster analysis of longitudinal profiles with subgroups
zbMATH Open document ID: 1393.62032
DOI: 10.1214/17-EJS1389
author: Xiaolu Zhu
author: Annie Qu
published in: Electronic Journal of Statistics
publication date: 2018-02-20T00:00:00Z
full work available at URL: https://projecteuclid.org/euclid.ejs/1517367715
review text: The authors propose a new approach to cluster longitudinal data based on nonparametric B-splines. The pairwise distance of the B-spline coefficients is used to group the longitudinal profiles, where the number of clusters is not specified in advance. The new method can be applied to unbalanced data sets, meaning that the longitudinal profiles of the subjects may consist of different numbers of points. The splines are fitted by a penalized regression and the pairwise distances of the spline coefficients is penalized to encourage subjects to fall into the same group. The authors establish the rate of convergence of these estimators and the recovery rate of the subgroups (depending on the distance between the groups). The results are illustrated in a simulation study and in a data example consisting of the weakly sales of different products over a total time of 11 years.
reviewed by: Martin Wendler
Mathematics Subject Classification ID: 62H30
Mathematics Subject Classification ID: 62G08
Mathematics Subject Classification ID: 62P20
zbMATH DE Number: 6841002
zbMATH Keywords: clustering
zbMATH Keywords: spline
zbMATH Keywords: penalized regression
zbMATH Keywords: longitudinal data
zbMATH Keywords: minimax concave penalty
zbMATH Keywords: model selection
zbMATH Keywords: nonparametric spline method
describes a project that uses: AS 136
MaRDI profile type: Publication
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OpenAlex ID: W2785880534
description: scientific article; zbMATH DE number 6841002