Clustering data with non-ignorable missingness using semi-parametric mixture models assuming independence within components-Reference-Cited by-同舟云学术

Clustering data with non-ignorable missingness using semi-parametric mixture models assuming independence within components

Published:2023-02-12 Issue:4 Volume:17 Page:1081-1122
ISSN:1862-5347
Container-title:Advances in Data Analysis and Classification
language:en
Short-container-title:Adv Data Anal Classif

Author:

du Roy de Chaumaray Marie,Marbac Matthieu

Publisher

Springer Science and Business Media LLC

Subject

Applied Mathematics,Computer Science Applications,Statistics and Probability

Link

https://link.springer.com/content/pdf/10.1007/s11634-023-00534-w.pdf

Reference48 articles.

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3. Audigier V, Niang N, Resche-Rigon M (2021) Clustering with missing data: which imputation model for which cluster analysis method? arXiv:2106.04424

4. Basagaña X, Barrera-Gómez J, Benet M, Antó JM, Garcia-Aymerich J (2013) A framework for multiple imputation in cluster analysis. Am J Epidemiol 177(7):718–725

5. Benaglia T, Chauveau D, Hunter DR (2009) An EM-like algorithm for semi-and nonparametric estimation in multivariate mixtures. J Comput Graph Stat 18(2):505–526

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