Robust likelihood inference for multivariate correlated count data

Research output: Contribution to journalArticlepeer-review

2 Scopus citations


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
Issue number3
StatePublished - 1 Sep 2016


  • Model misspecification
  • Multivariate negative binomial
  • Profile likelihood


Dive into the research topics of 'Robust likelihood inference for multivariate correlated count data'. Together they form a unique fingerprint.

Cite this