Some EM-type algorithms for incomplete data model building - Inria - Institut national de recherche en sciences et technologies du numérique Access content directly
Preprints, Working Papers, ... Year : 2021

Some EM-type algorithms for incomplete data model building

Abstract

We propose an extension of the EM algorithm and its stochastic versions for the construction of incomplete data models when the selected model minimizes a penalized likelihood criterion. This optimization problem is particularly challenging in the context of incomplete data, even when the model is relatively simple. However, by completing the data, the E-step of the algorithm allows us to simplify this problem of complete model selection into a classical problem of complete model selection that does not pose any major difficulties. We then show that the criterion to be minimized decreases with each iteration of the algorithm. Examples of the use of these algorithms are presented for the identification of regression mixture models and the construction of nonlinear mixed-effects models.
Fichier principal
Vignette du fichier
EMbuild_method.pdf (366.46 Ko) Télécharger le fichier
Origin Files produced by the author(s)

Dates and versions

hal-03512130 , version 1 (05-01-2022)

Identifiers

  • HAL Id : hal-03512130 , version 1

Cite

Marc Lavielle. Some EM-type algorithms for incomplete data model building. 2021. ⟨hal-03512130⟩
64 View
96 Download

Share

Gmail Mastodon Facebook X LinkedIn More