Handling Missing Values with Regularized Iterative Multiple Correspondence Analysis
| hal.structure.identifier | Institut de Recherche Mathématique de Rennes [IRMAR] | |
| dc.contributor.author | JOSSE, Julie | |
| hal.structure.identifier | Quality control and dynamic reliability [CQFD] | |
| dc.contributor.author | CHAVENT, Marie | |
| hal.structure.identifier | Université Bordeaux Segalen - Bordeaux 2 | |
| dc.contributor.author | LIQUET, Benoit | |
| hal.structure.identifier | Institut de Recherche Mathématique de Rennes [IRMAR] | |
| dc.contributor.author | HUSSON, François | |
| dc.date.accessioned | 2024-04-04T02:23:30Z | |
| dc.date.available | 2024-04-04T02:23:30Z | |
| dc.date.issued | 2012 | |
| dc.identifier.issn | 0176-4268 | |
| dc.identifier.uri | https://oskar-bordeaux.fr/handle/20.500.12278/189743 | |
| dc.description.abstractEn | A common approach to deal with missing values in multivariate exploratory data analysis consists in minimizing the loss function over all non-missing elements. This can be achieved by EM-type algorithms where an iterative imputation of the missing values is performed during the estimation of the axes and components. This paper proposes such an algorithm, named iterative multiple correspondence analysis, to handle missing values in multiple correspondence analysis (MCA). This algorithm, based on an iterative PCA algorithm, is described and its properties are studied. We point out the over tting problem and propose a regularized version of the algorithm to overcome this major issue. Finally, performances of the regularized iterative MCA algorithm (implemented in the R-package named missMDA) are assessed from both simulations and a real dataset. Results are promising with respect to other methods such as the missing-data passive modi ed margin method, an adaptation of the missing passive method used in Gi 's Homogeneity analysis framework. | |
| dc.language.iso | en | |
| dc.publisher | Springer Verlag | |
| dc.title.en | Handling Missing Values with Regularized Iterative Multiple Correspondence Analysis | |
| dc.type | Article de revue | |
| dc.identifier.doi | 10.1007/s00357-012-9097-0 | |
| dc.subject.hal | Statistiques [stat]/Calcul [stat.CO] | |
| bordeaux.journal | Journal of Classification | |
| bordeaux.page | 91-116 | |
| bordeaux.volume | 29 | |
| bordeaux.hal.laboratories | Institut de Mathématiques de Bordeaux (IMB) - UMR 5251 | * |
| bordeaux.issue | 1 | |
| bordeaux.institution | Université de Bordeaux | |
| bordeaux.institution | Bordeaux INP | |
| bordeaux.institution | CNRS | |
| bordeaux.peerReviewed | oui | |
| hal.identifier | hal-00763227 | |
| hal.version | 1 | |
| hal.popular | non | |
| hal.audience | Internationale | |
| hal.origin.link | https://hal.archives-ouvertes.fr//hal-00763227v1 | |
| bordeaux.COinS | ctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.jtitle=Journal%20of%20Classification&rft.date=2012&rft.volume=29&rft.issue=1&rft.spage=91-116&rft.epage=91-116&rft.eissn=0176-4268&rft.issn=0176-4268&rft.au=JOSSE,%20Julie&CHAVENT,%20Marie&LIQUET,%20Benoit&HUSSON,%20Fran%C3%A7ois&rft.genre=article |
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