Spatial modelling of claim frequency and claim size in non-life insurance

By Susanne Gschlößl, Claudia Czado. 2007

108 citations recorded by the source corpus.

Subfields: Demography, Economics and Econometrics, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/c3acf0762cbe2db70d3f2f33558f3453d2edb998

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Demography

1. Lu Yang
2. Gee Y. Lee
3. Edward W. Frees
4. Eike C. Brechmann
5. Aleksey Min
6. Rainer Kastenmeier
7. Claudia Czado

In Economics and Econometrics

1. Eike C. Brechmann
2. Claudia Czado
3. Aleksey Min
4. Lu Yang
5. Gee Y. Lee
6. Edward W. Frees
7. Rainer Kastenmeier
8. Nicole Kraemer

In Management Science and Operations Research

1. Lu Yang
2. Gee Y. Lee
3. Edward W. Frees
4. Eike C. Brechmann
5. Aleksey Min
6. Rainer Kastenmeier
7. Claudia Czado
8. Jun Yan

Citations in the corpus

• A mixed copula model for insurance claims and claim sizes (2012, methodology): “Gschlößl and Czado (2007) relax this assumption a bit by allowing the number of claims to enter as a covariate into the model for average claim size. In order to allow for more general dependency we construct a joint regression model by linking a marginal Gamma GLM for the average claim size and a m”
• Total loss estimation using copula-based regression models (2012, background): “Gschlößl and Czado (2007) therefore included the number of claims as a covariate into the model for average claim size.”
• Multivariate Frequency-Severity Regression Models in Insurance (2016, methodology): “For another application, a Bayesian approach for modeling claim frequency and size was proposed in [52], with both covariates as well as spatial random effects taken into account.”