Description Usage Arguments Details Value Note Author(s) References See Also Examples
Density of the policy loss
1 | dpolicy_loss(l, mu, delta, lambda, theta, family, y.max = 300,zt=TRUE)
|
l |
vector at which the density is evaluated |
mu |
expectation of the Gamma distribution |
delta |
dispersion parameter of the Gamma distribution |
lambda |
parameter of the (zero-truncated) Poisson distribution |
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, see below for details |
zt |
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.
density, evaluated at the vector l
lambda and mu can be scalars, or vectors of the same length as l
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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