Non-life rate-making with Bayesian GAMs

By Michel Denuit, Stefan Lang. 2004

77 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/0a3203e7e669e8860f3bc854ab17aa0e9d1d4fbc

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Citations in the corpus

Risk Classification for Claim Counts (2007, background): “References for risk classification include, for example, Dionne and Vanasse (1989) Dionne and Vanasse (1992), Dean, Lawless, and Willmot (1989), Denuit and Lang (2004), Gourieroux and Jasiak (2004), and Yip and Yau (2005).”
Statistical concepts of a priori and a posteriori risk classification in insurance (2011, background): “Denuit and Lang (2004) provides for an excellent discussion of ratemaking with additive regression models.”
Fixed versus Random Effects in Poisson Regression Models for Claim Counts: A Case Study with Motor Insurance (2006, methodology): “DENUIT & LANG (2004) used Generalised Additive Models.”
Nonlife Ratemaking and Risk Management with Bayesian Additive Models for Location, Scale and Shape (2013, methodology): “In conclusion, NB and ZIP model both give reasonable fits and are clearly preferable to the classical Poisson approach of Denuit and Lang (2004).”
Bayesian Structured Additive Distributional Regression (2013, background): “…example in explaining the number of citations of patents based on patent characteristics, in predicting the number of insurance claims of policyholders on the basis of previous claim histories [Denuit and Lang, 2004], or in modelling mortality due to a specific type of diseases (disease mapping).”
Bayesian Generalized Additive Models for Location, Scale, and Shape for Zero-Inflated and Overdispersed Count Data (2015, methodology): “The dataset has already been treated in Denuit and Lang (2004) who applied geoadditive Poisson models.”
Actuarial statistics with generalized linear mixed models (2007, methodology): “In the GLMM speciflcation weights are introduced by replacing ` with `=wij in f(yijjfl;bi;`) (see ( 10 )).”