GeneralizedPareto {actuar} | R Documentation |

Density function, distribution function, quantile function, random generation,
raw moments and limited moments for the Generalized Pareto
distribution with parameters `shape1`

, `shape2`

and
`scale`

.

dgenpareto(x, shape1, shape2, rate = 1, scale = 1/rate, log = FALSE) pgenpareto(q, shape1, shape2, rate = 1, scale = 1/rate, lower.tail = TRUE, log.p = FALSE) qgenpareto(p, shape1, shape2, rate = 1, scale = 1/rate, lower.tail = TRUE, log.p = FALSE) rgenpareto(n, shape1, shape2, rate = 1, scale = 1/rate) mgenpareto(order, shape1, shape2, rate = 1, scale = 1/rate) levgenpareto(limit, shape1, shape2, rate = 1, scale = 1/rate, order = 1)

`x, q` |
vector of quantiles. |

`p` |
vector of probabilities. |

`n` |
number of observations. If |

`shape1, shape2, scale` |
parameters. Must be strictly positive. |

`rate` |
an alternative way to specify the scale. |

`log, log.p` |
logical; if |

`lower.tail` |
logical; if |

`order` |
order of the moment. |

`limit` |
limit of the loss variable. |

The Generalized Pareto distribution with parameters `shape1`

*= a*, `shape2`

*= b* and `scale`

*= s* has density:

*
f(x) = Gamma(a + b)/(Gamma(a) * Gamma(b)) *
(s^a x^(b - 1))/(x + s)^(a + b)*

for *x > 0*, *a > 0*, *b > 0* and
*s > 0*.
(Here *Γ(α)* is the function implemented
by **R**'s `gamma()`

and defined in its help.)

The Generalized Pareto is the distribution of the random variable

*θ (X/(1 - X)),*

where *X* has a beta distribution with parameters *α*
and *τ*.

The Generalized Pareto distribution has the following special cases:

A Pareto distribution when

`shape2 == 1`

;An Inverse Pareto distribution when

`shape1 == 1`

.

The *k*th raw moment of the random variable *X* is
*E[X^k]*, *-shape2 < k < shape1*.

The *k*th limited moment at some limit
*d* is *E[min(X, d)^k]*,
*k > -shape2* and *shape1 - k* not a
negative integer.

`dgenpareto`

gives the density,
`pgenpareto`

gives the distribution function,
`qgenpareto`

gives the quantile function,
`rgenpareto`

generates random deviates,
`mgenpareto`

gives the *k*th raw moment, and
`levgenpareto`

gives the *k*th moment of the limited loss
variable.

Invalid arguments will result in return value `NaN`

, with a warning.

`levgenpareto`

computes the limited expected value using
`betaint`

.

Distribution also known as the Beta of the Second Kind. See also Kleiber and Kotz (2003) for alternative names and parametrizations.

The Generalized Pareto distribution defined here is different from the one in Embrechts et al. (1997) and in Wikipedia; see also Kleiber and Kotz (2003, section 3.12). One may most likely compute quantities for the latter using functions for the Pareto distribution with the appropriate change of parametrization.

The `"distributions"`

package vignette provides the
interrelations between the continuous size distributions in
actuar and the complete formulas underlying the above functions.

Vincent Goulet vincent.goulet@act.ulaval.ca and Mathieu Pigeon

Embrechts, P., Klüppelberg, C. and Mikisch, T. (1997), *Modelling
Extremal Events for Insurance and Finance*, Springer.

Kleiber, C. and Kotz, S. (2003), *Statistical Size Distributions
in Economics and Actuarial Sciences*, Wiley.

Klugman, S. A., Panjer, H. H. and Willmot, G. E. (2012),
*Loss Models, From Data to Decisions, Fourth Edition*, Wiley.

exp(dgenpareto(3, 3, 4, 4, log = TRUE)) p <- (1:10)/10 pgenpareto(qgenpareto(p, 3, 3, 1), 3, 3, 1) qgenpareto(.3, 3, 4, 4, lower.tail = FALSE) ## variance mgenpareto(2, 3, 2, 1) - mgenpareto(1, 3, 2, 1)^2 ## case with shape1 - order > 0 levgenpareto(10, 3, 3, scale = 1, order = 2) ## case with shape1 - order < 0 levgenpareto(10, 1.5, 3, scale = 1, order = 2)

[Package *actuar* version 3.1-4 Index]