Clustering Data with Nonignorable Missingness Using Semi-Parametric Mixture Models
Abstract
We are concerned in clustering continuous data sets subject to nonignorable missingness. We per- form clustering with a specific semi-parametric mixture, avoiding the component distributions and the missingness process to be specified, un- der the assumption of conditional independence given the component. Estimation is performed by maximizing an extension of smoothed likeli- hood allowing missingness. This optimization is achieved by a Majorization-Minorization algo- rithm. We illustrate the relevance of the approach by numerical experiments.
Cite
Text
de Chaumaray and Marbac. "Clustering Data with Nonignorable Missingness Using Semi-Parametric Mixture Models." ICML 2020 Workshops: Artemiss, 2020.Markdown
[de Chaumaray and Marbac. "Clustering Data with Nonignorable Missingness Using Semi-Parametric Mixture Models." ICML 2020 Workshops: Artemiss, 2020.](https://mlanthology.org/icmlw/2020/dechaumaray2020icmlw-clustering/)BibTeX
@inproceedings{dechaumaray2020icmlw-clustering,
title = {{Clustering Data with Nonignorable Missingness Using Semi-Parametric Mixture Models}},
author = {de Chaumaray, Marie Du Roy and Marbac, Matthieu},
booktitle = {ICML 2020 Workshops: Artemiss},
year = {2020},
url = {https://mlanthology.org/icmlw/2020/dechaumaray2020icmlw-clustering/}
}