Clustering multiply imputed multivariate high‐dimensional longitudinal profiles

DOI10.1002/bimj.201500027zbMath1391.62113OpenAlexW2607775638WikidataQ38823781 ScholiaQ38823781MaRDI QIDQ5364042

Liesbeth Bruckers, Geert Molenberghs, Paul Dendale

Publication date: 2 October 2017

Published in: Biometrical Journal (Search for Journal in Brave)

Full work available at URL: http://hdl.handle.net/1942/24959

zbMATH Keywords

data reductioncluster analysismissing datafunctional data analysis

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30) Applications of statistics to biology and medical sciences; meta analysis (62P10)

Related Items (4)

Clustering with missing data: which equivalent for Rubin's rules? ⋮ Multiobjective semisupervised learning with a right‐censored endpoint adapted to the multiple imputation framework ⋮ Clustering with missing and left‐censored data: A simulation study comparing multiple‐imputation‐based procedures ⋮ Clustering Data with Nonignorable Missingness using Semi-Parametric Mixture Models

This page was built for publication: Clustering multiply imputed multivariate high‐dimensional longitudinal profiles