Factor models for multivariate count data

Michel Wedel*, Ulf Böckenholt, Wagner A. Kamakura

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

42 Scopus citations


We develop a general class of factor-analytic models for the analysis of multivariate (truncated) count data. Dependencies in multivariate counts are of interest in many applications, but few approaches have been proposed for their analysis. Our model class allows for a variety of distributions of the factors in the exponential family. The proposed framework includes a large number of previously proposed factor and random effect models as special cases and leads to many new models that have not been considered so far. Whereas previously these models were proposed separately as different cases, our framework unifies these models and enables one to study them simultaneously. We estimate the Poisson factor models with the method of simulated maximum likelihood. A Monte-Carlo study investigates the performance of this approach in terms of estimation bias and precision. We illustrate the approach in an analysis of TV channels data.

Original languageEnglish (US)
Pages (from-to)356-369
Number of pages14
JournalJournal of Multivariate Analysis
Issue number2
StatePublished - Nov 2003


  • Link function
  • Poisson distribution
  • Simulated likehood

ASJC Scopus subject areas

  • Statistics and Probability
  • Numerical Analysis
  • Statistics, Probability and Uncertainty


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