actuar (version 3.3-4)

InverseParalogistic: The Inverse Paralogistic Distribution

Description

Density function, distribution function, quantile function, random generation, raw moments and limited moments for the Inverse Paralogistic distribution with parameters shape and scale.

Usage

dinvparalogis(x, shape, rate = 1, scale = 1/rate, log = FALSE)
pinvparalogis(q, shape, rate = 1, scale = 1/rate,
              lower.tail = TRUE, log.p = FALSE)
qinvparalogis(p, shape, rate = 1, scale = 1/rate,
              lower.tail = TRUE, log.p = FALSE)
rinvparalogis(n, shape, rate = 1, scale = 1/rate)
minvparalogis(order, shape, rate = 1, scale = 1/rate)
levinvparalogis(limit, shape, rate = 1, scale = 1/rate,
                order = 1)

Value

dinvparalogis gives the density,

pinvparalogis gives the distribution function,

qinvparalogis gives the quantile function,

rinvparalogis generates random deviates,

minvparalogis gives the \(k\)th raw moment, and

levinvparalogis gives the \(k\)th moment of the limited loss variable.

Invalid arguments will result in return value NaN, with a warning.

Arguments

x, q

vector of quantiles.

p

vector of probabilities.

n

number of observations. If length(n) > 1, the length is taken to be the number required.

shape, scale

parameters. Must be strictly positive.

rate

an alternative way to specify the scale.

log, log.p

logical; if TRUE, probabilities/densities \(p\) are returned as \(\log(p)\).

lower.tail

logical; if TRUE (default), probabilities are \(P[X \le x]\), otherwise, \(P[X > x]\).

order

order of the moment.

limit

limit of the loss variable.

Author

Vincent Goulet and Mathieu Pigeon

Details

The inverse paralogistic distribution with parameters shape \(= \tau\) and scale \(= \theta\) has density: $$f(x) = \frac{\tau^2 (x/\theta)^{\tau^2}}{% x [1 + (x/\theta)^\tau]^{\tau + 1}}$$ for \(x > 0\), \(\tau > 0\) and \(\theta > 0\).

The \(k\)th raw moment of the random variable \(X\) is \(E[X^k]\), \(-\tau^2 < k < \tau\).

The \(k\)th limited moment at some limit \(d\) is \(E[\min(X, d)^k]\), \(k > -\tau^2\) and \(1 - k/\tau\) not a negative integer.

References

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.

Examples

exp(dinvparalogis(2, 3, 4, log = TRUE))
p <- (1:10)/10
pinvparalogis(qinvparalogis(p, 2, 3), 2, 3)

## first negative moment
minvparalogis(-1, 2, 2)

## case with 1 - order/shape > 0
levinvparalogis(10, 2, 2, order = 1)

## case with 1 - order/shape < 0
levinvparalogis(10, 2/3, 2, order = 1)