construct_tariff_classes: Construct insurance tariff classes

In insurancerating: Analytic Insurance Rating Techniques

Construct insurance tariff classes

Description

Constructs insurance tariff classes to fitgam objects produced by fit_gam. The goal is to bin the continuous risk factors such that categorical risk factors result which capture the effect of the covariate on the response in an accurate way, while being easy to use in a generalized linear model (GLM).

Constructs insurance tariff classes to fitgam objects produced by fit_gam. The goal is to bin the continuous risk factors such that categorical risk factors result which capture the effect of the covariate on the response in an accurate way, while being easy to use in a generalized linear model (GLM).

Usage

construct_tariff_classes(
  object,
  alpha = 0,
  niterations = 10000,
  ntrees = 200,
  seed = 1
)

construct_tariff_classes(
  object,
  alpha = 0,
  niterations = 10000,
  ntrees = 200,
  seed = 1
)

Arguments

object	fitgam object produced by fit_gam
alpha	complexity parameter. The complexity parameter (alpha) is used to control the number of tariff classes. Higher values for alpha render less tariff classes. (alpha = 0 is default).
niterations	in case the run does not converge, it terminates after a specified number of iterations defined by niterations.
ntrees	the number of trees in the population.
seed	an numeric seed to initialize the random number generator (for reproducibility).

Details

Evolutionary trees are used as a technique to bin the fitgam object produced by fit_gam into risk homogeneous categories. This method is based on the work by Henckaerts et al. (2018). See Grubinger et al. (2014) for more details on the various parameters that control aspects of the evtree fit.

Evolutionary trees are used as a technique to bin the fitgam object produced by fit_gam into risk homogeneous categories. This method is based on the work by Henckaerts et al. (2018). See Grubinger et al. (2014) for more details on the various parameters that control aspects of the evtree fit.

Value

A list of class constructtariffclasses with components

prediction	data frame with predicted values
x	name of continuous risk factor for which tariff classes are constructed
model	either 'frequency', 'severity' or 'burning'
data	data frame with predicted values and observed values
x_obs	observations for continuous risk factor
splits	vector with boundaries of the constructed tariff classes
tariff_classes	values in vector x coded according to which constructed tariff class they fall

A list of class constructtariffclasses with components

prediction	data frame with predicted values
x	name of continuous risk factor for which tariff classes are constructed
model	either 'frequency', 'severity' or 'burning'
data	data frame with predicted values and observed values
x_obs	observations for continuous risk factor
splits	vector with boundaries of the constructed tariff classes
tariff_classes	values in vector x coded according to which constructed tariff class they fall

Author(s)

Martin Haringa

References

Antonio, K. and Valdez, E. A. (2012). Statistical concepts of a priori and a posteriori risk classification in insurance. Advances in Statistical Analysis, 96(2):187–224. doi:10.1007/s10182-011-0152-7.

Grubinger, T., Zeileis, A., and Pfeiffer, K.-P. (2014). evtree: Evolutionary learning of globally optimal classification and regression trees in R. Journal of Statistical Software, 61(1):1–29. doi:10.18637/jss.v061.i01.

Henckaerts, R., Antonio, K., Clijsters, M. and Verbelen, R. (2018). A data driven binning strategy for the construction of insurance tariff classes. Scandinavian Actuarial Journal, 2018:8, 681-705. doi:10.1080/03461238.2018.1429300.

Wood, S.N. (2011). Fast stable restricted maximum likelihood and marginal likelihood estimation of semiparametric generalized linear models. Journal of the Royal Statistical Society (B) 73(1):3-36. doi:10.1111/j.1467-9868.2010.00749.x.

Antonio, K. and Valdez, E. A. (2012). Statistical concepts of a priori and a posteriori risk classification in insurance. Advances in Statistical Analysis, 96(2):187–224. doi:10.1007/s10182-011-0152-7.

Grubinger, T., Zeileis, A., and Pfeiffer, K.-P. (2014). evtree: Evolutionary learning of globally optimal classification and regression trees in R. Journal of Statistical Software, 61(1):1–29. doi:10.18637/jss.v061.i01.

Henckaerts, R., Antonio, K., Clijsters, M. and Verbelen, R. (2018). A data driven binning strategy for the construction of insurance tariff classes. Scandinavian Actuarial Journal, 2018:8, 681-705. doi:10.1080/03461238.2018.1429300.

Wood, S.N. (2011). Fast stable restricted maximum likelihood and marginal likelihood estimation of semiparametric generalized linear models. Journal of the Royal Statistical Society (B) 73(1):3-36. doi:10.1111/j.1467-9868.2010.00749.x.

Examples

## Not run: 
library(dplyr)
fit_gam(MTPL, nclaims = nclaims, x = age_policyholder, exposure = exposure) %>%
   construct_tariff_classes(.)

## End(Not run)

## Not run: 
library(dplyr)
fit_gam(MTPL, nclaims = nclaims, x = age_policyholder,
exposure = exposure) %>%
   construct_tariff_classes(.)

## End(Not run)

insurancerating documentation built on Dec. 28, 2022, 2:53 a.m.