Extended Pareto | R Documentation |
Density, distribution function, quantile function and random generation for the Extended Pareto Distribution (EPD).
depd(x, gamma, kappa, tau = -1, log = FALSE)
pepd(x, gamma, kappa, tau = -1, lower.tail = TRUE, log.p = FALSE)
qepd(p, gamma, kappa, tau = -1, lower.tail = TRUE, log.p = FALSE)
repd(n, gamma, kappa, tau = -1)
x |
Vector of quantiles. |
p |
Vector of probabilities. |
n |
Number of observations. |
gamma |
The |
kappa |
The |
tau |
The |
log |
Logical indicating if the densities are given as |
lower.tail |
Logical indicating if the probabilities are of the form |
log.p |
Logical indicating if the probabilities are given as |
The Cumulative Distribution Function (CDF) of the EPD is equal to
F(x) = 1-(x(1+\kappa-\kappa x^{\tau}))^{-1/\gamma}
for all x > 1
and F(x)=0
otherwise.
Note that an EPD random variable with \tau=-1
and \kappa=\gamma/\sigma-1
is GPD distributed with \mu=1
, \gamma
and \sigma
.
depd
gives the density function evaluated in x
, pepd
the CDF evaluated in x
and qepd
the quantile function evaluated in p
. The length of the result is equal to the length of x
or p
.
repd
returns a random sample of length n
.
Tom Reynkens.
Beirlant, J., Joossens, E. and Segers, J. (2009). "Second-Order Refined Peaks-Over-Threshold Modelling for Heavy-Tailed Distributions." Journal of Statistical Planning and Inference, 139, 2800–2815.
Pareto
, GPD
, Distributions
# Plot of the PDF
x <- seq(0, 10, 0.01)
plot(x, depd(x, gamma=1/2, kappa=1, tau=-1), xlab="x", ylab="PDF", type="l")
# Plot of the CDF
x <- seq(0, 10, 0.01)
plot(x, pepd(x, gamma=1/2, kappa=1, tau=-1), xlab="x", ylab="CDF", type="l")
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