Robust likelihood inference for multivariate correlated count data

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Abstract

A parametric robust approach for analyzing correlated count data is introduced. This method enables one to construct an asymptotically valid likelihood for the regression parameter when knowledge about the joint distribution for data is scarce or not available. We use simulations and real data analysis to demonstrate the merit of the proposed robust likelihood method.

Original languageEnglish
Pages (from-to)845-857
Number of pages13
JournalComputational Statistics
Volume31
Issue number3
DOIs
StatePublished - 1 Sep 2016

Keywords

  • Model misspecification
  • Multivariate negative binomial
  • Profile likelihood

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