Description Usage Arguments Details Value Author(s) References See Also Examples
Cumulative distribution function of the policy loss
1 | ppolicy_loss(l, mu, delta, lambda, theta, family, y.max = 20,zt=TRUE)
|
l |
vector at which the distribution is evaluated |
mu |
expectation of the Gamma distribution |
delta |
dispersion parameter of the Gamma distribution |
lambda |
parameter of the ZTP 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 in the density, see below for more 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.
distribution function, evaluated at 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.
epolicy_loss
, qpolicy_loss
, dpolicy_loss
1 2 3 4 5 6 7 8 9 |
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