nlirms.family: Family objects for designing rate-making system

In nlirms: Non-Life Insurance Rate-Making System

Description

Current available distributions that can be used to designing of rate-making system .

Usage

## Default S3 method:
nlirms.family(object, ...)
nlirms.family(object, ...)
as.nlirms.family(object)

Arguments

object	a nlirms.family object
...	for further arguments

Details

there are several freuency distributions available to designing of rate-making system as follow:

Poissin-Gamma or Negative Binomial (PGA model)

Poissin-Inverse Gamma (PIGA model)

Poissin-Generalized Inverse Gaussian or Sichel (PGIG model)

Poissin-Inverse Gaussian (PGIG model reduce to the Poissin-Inverse Gaussian model for nu=-.5)

Poissin-Harmonic (PGIG model reduce to the Poissin-Harmonic model for nu=0)

there are several severity distributions available to designing of rate-making system as follow:

Exponential-Gamma (EGA model)

Exponential-Inverse Gamma or Pareto (EIGA model)

Exponential-Generalized Inverse Gaussian (EGIG model)

Exponential-Inverse Gaussian (EGIG model reduce to the Exponential-Inverse Gaussian model for nu=-.5)

Exponential-Harmonic (EGIG model reduce to the Exponential-Harmonic model for nu=0)

Author(s)

Saeed Mohammadpour ([e-mail]), Soodabeh Mohammadpoor Golojeh ([e-mail])

References

Frangos, N. E., & Vrontos, S. D. (2001). Design of optimal bonus-malus systems with a frequency and a severity component on an individual basis in automobile insurance. ASTIN Bulletin: The Journal of the IAA, 31(1), 1-22.

Lemaire, J. (1995) Bonus-Malus Systems in Automobile Insurance, Kluwer Academic Publishers, Massachusetts.

MohammadPour, S., Saeedi, K., & Mahmoudvand, R. (2017). Bonus-Malus System Using Finite Mixture Models. Statistics, Optimization & Information Computing, 5(3), 179-187.

Najafabadi, A. T. P., & MohammadPour, S. (2017). A k-Inflated Negative Binomial Mixture Regression Model: Application to Rate–Making Systems. Asia-Pacific Journal of Risk and Insurance, 12.

Rigby, R. A., & Stasinopoulos, D. M. (2005). Generalized additive models for location, scale and shape. Journal of the Royal Statistical Society: Series C (Applied Statistics), 54(3), 507-554.

Stasinopoulos, D. M., Rigby, B. A., Akantziliotou, C., Heller, G., Ospina, R., & Motpan, N. (2010). gamlss. dist: Distributions to Be Used for GAMLSS Modelling. R package version, 4-0.

Stasinopoulos, D. M., & Rigby, R. A. (2007). Generalized additive models for location scale and shape (GAMLSS) in R. Journal of Statistical Software, 23(7), 1-46.

Examples

PGA(mu , sigma)
EGA(mu , sigma)

nlirms documentation built on May 1, 2019, 7:06 p.m.