Description Usage Arguments Details Value Author(s) References See Also Examples
Expectation and variance of the policy loss
1 | epolicy_loss(mu, delta, lambda, theta, family, y.max = 300,zt=TRUE,compute.var=FALSE)
|
mu |
expectation of the Gamma distribution, can be a vector |
delta |
dispersion parameter of the Gamma distribution |
lambda |
parameter of the (zero-truncated) Poisson distribution, can be a vector of the same length as mu |
theta |
copula parameter |
family |
an integer defining the bivariate copula family: 1 = Gauss, 3 = Clayton, 4=Gumbel, 5=Frank |
y.max |
upper value of the finite sum that we use to approximate the infinite sum in the density, see below for details |
zt |
logical. If |
compute.var |
logical. If |
For a Gamma distributed variable X and a (zero truncated) Possion variable Y, the policy loss is defined as L=X\cdot Y. Its density is an infinite sum of weighted Gamma densities. The parameter y.max
is the upper value of the finite sum that approximates the infinite sum.
mean |
expectation of the policy loss |
var |
variance of the policy loss |
Nicole Kraemer
N. Kraemer, E. Brechmann, D. Silvestrini, C. Czado (2013): Total loss estimation using copula-based regression models. Insurance: Mathematics and Economics 53 (3), 829 - 839.
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