The paper is framed within the literature around Louis’ identity for the observed information matrix in incomplete data problems, with a focus on the implied acceleration of maximum likelihood estimation for mixture models. The goal is twofold: to obtain direct expressions for standard errors of parameters from the EM algorithm and to reduce the computational burden of the estimation procedure for a class of mixture models with uncertainty for rating variables. This achievement fosters the feasibility of best-subset variable selection, which is an advisable strategy to identify response patterns from regression models for all Mixtures of Experts systems. The discussion is supported by simulation experiments and a real case study.

An accelerated EM algorithm for mixture models with uncertainty for rating data / Simone, Rosaria. - In: COMPUTATIONAL STATISTICS. - ISSN 0943-4062. - 36:1(2021), pp. 691-714. [10.1007/s00180-020-01004-z]

An accelerated EM algorithm for mixture models with uncertainty for rating data

Rosaria Simone
2021

Abstract

The paper is framed within the literature around Louis’ identity for the observed information matrix in incomplete data problems, with a focus on the implied acceleration of maximum likelihood estimation for mixture models. The goal is twofold: to obtain direct expressions for standard errors of parameters from the EM algorithm and to reduce the computational burden of the estimation procedure for a class of mixture models with uncertainty for rating variables. This achievement fosters the feasibility of best-subset variable selection, which is an advisable strategy to identify response patterns from regression models for all Mixtures of Experts systems. The discussion is supported by simulation experiments and a real case study.
2021
An accelerated EM algorithm for mixture models with uncertainty for rating data / Simone, Rosaria. - In: COMPUTATIONAL STATISTICS. - ISSN 0943-4062. - 36:1(2021), pp. 691-714. [10.1007/s00180-020-01004-z]
File in questo prodotto:
Non ci sono file associati a questo prodotto.

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11588/809897
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 7
  • ???jsp.display-item.citation.isi??? 6
social impact