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.
- Sprache
-
Englisch
- Erschienen in
-
Series: Discussion Paper ; No. 334
- Thema
-
Bayesian semiparametric count data regression
negative binomial distribution
Poisson-Gamma distribution
Poisson-Log-Normal distribution
MCMC
spatial models
- Ereignis
-
Geistige Schöpfung
- (wer)
-
Fahrmeir, Ludwig
Osuna, Leyre
- Ereignis
-
Veröffentlichung
- (wer)
-
Ludwig-Maximilians-Universität München, Sonderforschungsbereich 386 - Statistische Analyse diskreter Strukturen
- (wo)
-
München
- (wann)
-
2003
- DOI
-
doi:10.5282/ubm/epub.1712
- Handle
- URN
-
urn:nbn:de:bvb:19-epub-1712-0
- Letzte Aktualisierung
-
10.03.2025, 11:43 MEZ
Datenpartner
Dieses Objekt wird bereitgestellt von:
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Objekttyp
- Arbeitspapier
Beteiligte
- Fahrmeir, Ludwig
- Osuna, Leyre
- Ludwig-Maximilians-Universität München, Sonderforschungsbereich 386 - Statistische Analyse diskreter Strukturen
Entstanden
- 2003
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