An INAR(1) negative multinomial regression model for longitudinal count data

Ulf Böckenholt*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

12 Scopus citations

Abstract

This paper discusses a regression model for the analysis of longitudinal count data observed in a panel study. An integer-valued first-order autoregressive [INAR(1)] Poisson process is adapted to represent time-dependent correlations among the counts. By combining the INAR(1)-representation with a random effects approach, a new negative multinomial distribution is derived that includes the bivariate negative binomial distribution proposed by Edwards and Gurland (1961) and Subrahmaniam (1966) as a special case. A detailed analysis of the relationship between personality factors and daily emotion experiences illustrates the approach.

Original languageEnglish (US)
Pages (from-to)53-67
Number of pages15
JournalPsychometrika
Volume64
Issue number1
DOIs
StatePublished - Mar 1999

Funding

This research was partially supported by NSF grant SBR-9409531. The author is grateful to Ulrich Schimmack and Ed Diener for providing the data set used in the application section and for helpful comments on this research. Requests for reprints should be sent to Ulf B6ckenholt, University of Illinois at Urbana-Champaign, Department of Psychology, 603 East Daniel Street, Champaign, IL 61820.

Keywords

  • Binomial thinning
  • Markov models
  • Poisson distribution

ASJC Scopus subject areas

  • General Psychology
  • Applied Mathematics

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