actuaRE-package {actuaRE} | R Documentation |
Handling Hierarchically Structured Risk Factors using Random Effects Models
Description
Using this package, you can fit a random effects model using either the hierarchical credibility model, a combination of the hierarchical credibility model with a generalized linear model or a Tweedie generalized linear mixed model. See Campo, B.D.C. and Antonio, K. (2023) <doi:10.1080/03461238.2022.2161413>.
References
Campo, B.D.C. and Antonio, Katrien (2023). Insurance pricing with hierarchically structured data an illustration with a workers' compensation insurance portfolio. Scandinavian Actuarial Journal, doi: 10.1080/03461238.2022.2161413
Dannenburg, D. R., Kaas, R. and Goovaerts, M. J. (1996). Practical actuarial credibility models. Amsterdam: IAE (Institute of Actuarial Science and Econometrics of the University of Amsterdam).
Jewell, W. S. (1975). The use of collateral data in credibility theory: a hierarchical model. Laxenburg: IIASA.
Ohlsson, E. (2005). Simplified estimation of structure parameters in hierarchical credibility. Presented at the Zurich ASTIN Colloquium.http://www.actuaries.org/ASTIN/Colloquia/Zurich/Ohlsson.pdf
Ohlsson, E. (2008). Combining generalized linear models and credibility models in practice. Scandinavian Actuarial Journal 2008(4), 301–314.
See Also
hierCredibility
hierCredGLM
hierCredTweedie
tweedieGLMM
BalanceProperty
Examples
library(actuaRE)
# Vignette of the package
vignette(package = "actuaRE")
# Load data
data(hachemeisterLong)
data(dataCar)
# Hierarchical credibility model of Jewell
fit = hierCredibility(ratio, weight, cohort, state, hachemeisterLong)
# Combination of the hierarchical credibility model with a GLM (Ohlsson, 2008)
fit = hierCredGLM(Y ~ area + (1 | VehicleType / VehicleBody), dataCar, weights = w,
p = 1.7)