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

Rosaria Simone ()
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Rosaria Simone: University of Naples Federico II

Computational Statistics, 2021, vol. 36, issue 1, No 29, 714 pages

Abstract: 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.

Keywords: Louis’ Identity; Accelerated EM algorithm; cub Mixture models; Rating data; Standard errors (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (3)

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DOI: 10.1007/s00180-020-01004-z

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