Arbeitspapier

Structured count data regression

Overdispersion in count data regression is often caused by neglection or inappropriate modelling of individual heterogeneity, temporal or spatial correlation, and nonlinear covariate effects. In this paper, we develop and study semiparametric count data models which can deal with these issues by incorporating corresponding components in structured additive form into the predictor. The models are fully Bayesian and inference is carried out by computationally efficient MCMC techniques. In a simulation study, we investigate how well the different components can be identified with the data at hand. The approach is applied to a large data set of claim frequencies from car insurance.

Language
Englisch

Bibliographic citation
Series: Discussion Paper ; No. 334

Subject
Bayesian semiparametric count data regression
negative binomial distribution
Poisson-Gamma distribution
Poisson-Log-Normal distribution
MCMC
spatial models

Event
Geistige Schöpfung
(who)
Fahrmeir, Ludwig
Osuna, Leyre
Event
Veröffentlichung
(who)
Ludwig-Maximilians-Universität München, Sonderforschungsbereich 386 - Statistische Analyse diskreter Strukturen
(where)
München
(when)
2003

DOI
doi:10.5282/ubm/epub.1712
Handle
URN
urn:nbn:de:bvb:19-epub-1712-0
Last update
10.03.2025, 11:43 AM CET

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Object type

  • Arbeitspapier

Associated

  • Fahrmeir, Ludwig
  • Osuna, Leyre
  • Ludwig-Maximilians-Universität München, Sonderforschungsbereich 386 - Statistische Analyse diskreter Strukturen

Time of origin

  • 2003

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