Dealing with missing data through mixture models - Inria - Institut national de recherche en sciences et technologies du numérique Access content directly
Conference Papers Year : 2017

Dealing with missing data through mixture models

Abstract

Many data sets have missing values, however the majority of statistical methods need a complete dataset to work. Thus, practitioners often use imputation or multiple imputations to complete the data as a pre-processing step. In this talk it will be shown how mixture models can be used to naturally deal with missing data in an integrated way depending on the purpose. Especially, it will be shown how they can be used to classify the data or derive estimates for the distances. Results on real data will be shown.
Fichier principal
Vignette du fichier
vandewalle.pdf (249.41 Ko) Télécharger le fichier
slides2.pdf (869.96 Ko) Télécharger le fichier
Origin : Files produced by the author(s)

Dates and versions

hal-01667614 , version 1 (19-12-2017)

Identifiers

  • HAL Id : hal-01667614 , version 1

Cite

Vincent Vandewalle, Christophe Biernacki. Dealing with missing data through mixture models. ICB Seminars 2017 - 154th Seminar on ”Statistics and clinical practice”, May 2017, Varsovie, Poland. pp.1-3. ⟨hal-01667614⟩
79 View
380 Download

Share

Gmail Facebook X LinkedIn More