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  • Search: subject:"overdispersed count data"
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Year of publication
Subject
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MCMC 5 Negative Binomial 3 mixed Poisson regression 3 overdispersed count data 3 probabilistic forecasts 3 zero-adjusted models 3 zero-inflated Poisson 3 Forecasting model 2 Generalisiertes lineares Modell 2 Generalized linear model 2 Markov chain 2 Markov-Kette 2 Mixed Poisson regression 2 Monte Carlo simulation 2 Monte-Carlo-Simulation 2 Negative binomial 2 Overdispersed count data 2 Probabilistic forecasts 2 Probability theory 2 Prognoseverfahren 2 Regression analysis 2 Regressionsanalyse 2 Risikomanagement 2 Risk management 2 Statistical distribution 2 Statistische Verteilung 2 Stochastic process 2 Stochastischer Prozess 2 Theorie 2 Theory 2 Wahrscheinlichkeitsrechnung 2 Zero-adjusted models 2 Zero-inflated Poisson 2 Bayes-Statistik 1 Bayesian inference 1
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Online availability
All
Free 3 Undetermined 1
Type of publication
All
Book / Working Paper 3 Article 2
Type of publication (narrower categories)
All
Working Paper 2 Arbeitspapier 1 Article in journal 1 Aufsatz in Zeitschrift 1 Graue Literatur 1 Non-commercial literature 1
Language
All
English 3 Undetermined 2
Author
All
Denuit, Michel 5 Klein, Nadja 5 Kneib, Thomas 5 Lang, Stefan 5
Institution
All
Institut für Finanzwissenschaft, Fakultät für Volkswirtschaft und Statistik 1
Published in...
All
Insurance / Mathematics & economics 1 Insurance: Mathematics and Economics 1 Working Papers / Institut für Finanzwissenschaft, Fakultät für Volkswirtschaft und Statistik 1 Working Papers in Economics and Statistics 1 Working papers in economics and statistics 1
Source
All
ECONIS (ZBW) 2 RePEc 2 EconStor 1
Showing 1 - 5 of 5
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Nonlife ratemaking and risk management with Bayesian additive models for location, scale and shape
Klein, Nadja; Denuit, Michel; Lang, Stefan; Kneib, Thomas - 2013
Generalized additive models for location, scale and shape define a flexible, semi-parametric class of regression models for analyzing insurance data in which the exponential family assumption for the response is relaxed. This approach allows the actuary to include risk factors not only in the...
Persistent link: https://www.econbiz.de/10010397159
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Cover Image
Nonlife Ratemaking and Risk Management with Bayesian Additive Models for Location, Scale and Shape
Klein, Nadja; Denuit, Michel; Lang, Stefan; Kneib, Thomas - Institut für Finanzwissenschaft, Fakultät für … - 2013
Generalized additive models for location, scale and shape define a flexible, semi-parametric class of regression models for analyzing insurance data in which the exponential family assumption for the response is relaxed. This approach allows the actuary to include risk factors not only in the...
Persistent link: https://www.econbiz.de/10010699560
Saved in:
Cover Image
Nonlife ratemaking and risk management with Bayesian additive models for location, scale and shape
Klein, Nadja; Denuit, Michel; Lang, Stefan; Kneib, Thomas - 2013
Generalized additive models for location, scale and shape define a flexible, semi-parametric class of regression models for analyzing insurance data in which the exponential family assumption for the response is relaxed. This approach allows the actuary to include risk factors not only in the...
Persistent link: https://www.econbiz.de/10010190248
Saved in:
Cover Image
Nonlife ratemaking and risk management with Bayesian generalized additive models for location, scale, and shape
Klein, Nadja; Denuit, Michel; Lang, Stefan; Kneib, Thomas - In: Insurance: Mathematics and Economics 55 (2014) C, pp. 225-249
Generalized additive models for location, scale and, shape define a flexible, semi-parametric class of regression models for analyzing insurance data in which the exponential family assumption for the response is relaxed. This approach allows the actuary to include risk factors not only in the...
Persistent link: https://www.econbiz.de/10010753201
Saved in:
Cover Image
Nonlife ratemaking and risk management with Bayesian generalized additive models for location, scale, and shape
Klein, Nadja; Denuit, Michel; Lang, Stefan; Kneib, Thomas - In: Insurance / Mathematics & economics 55 (2014), pp. 225-249
Persistent link: https://www.econbiz.de/10010366171
Saved in:
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