On a hybrid data cloning method and its application in generalized linear mixed models

Hossein Baghishani*, Haavard Rue, Mohsen Mohammadzadeh

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

7 Scopus citations

Abstract

The data cloning method is a new computational tool for computing maximum likelihood estimates in complex statistical models such as mixed models. This method is synthesized with integrated nested Laplace approximation to compute maximum likelihood estimates efficiently via a fast implementation in generalized linear mixed models. Asymptotic behavior of the hybrid data cloning method is discussed. The performance of the proposed method is illustrated through a simulation study and real examples. It is shown that the proposed method performs well and rightly justifies the theory. Supplemental materials for this article are available online.

Original languageEnglish (US)
Pages (from-to)597-613
Number of pages17
JournalStatistics and Computing
Volume22
Issue number2
DOIs
StatePublished - Mar 1 2012

Keywords

  • Asymptotic normality
  • Data cloning
  • Generalized linear mixed models
  • Integrated nested Laplace approximation

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Statistics and Probability
  • Statistics, Probability and Uncertainty
  • Computational Theory and Mathematics

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