actuar: Burr – R documentation

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Burr

The Burr Distribution

Description

Density function, distribution function, quantile function, random generation, raw moments and limited moments for the Burr distribution with parameters shape1, shape2 and scale.

Usage

dburr(x, shape1, shape2, rate = 1, scale = 1/rate,
      log = FALSE)
pburr(q, shape1, shape2, rate = 1, scale = 1/rate,
      lower.tail = TRUE, log.p = FALSE)
qburr(p, shape1, shape2, rate = 1, scale = 1/rate,
      lower.tail = TRUE, log.p = FALSE)
rburr(n, shape1, shape2, rate = 1, scale = 1/rate)
mburr(order, shape1, shape2, rate = 1, scale = 1/rate)
levburr(limit, shape1, shape2, rate = 1, scale = 1/rate,
        order = 1)

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.
`shape1, shape2, 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 <= x], otherwise, P[X > x].
`order`	order of the moment.
`limit`	limit of the loss variable.

Details

The Burr distribution with parameters shape1 = a, shape2 = b and scale = s has density:

f(x) = (a b (x/s)^b)/(x [1 + (x/s)^b]^(a + 1))

for x > 0, a > 0, b > 0 and s > 0.

The Burr is the distribution of the random variable

s (X/(1 - X))^(1/b),

where X has a beta distribution with parameters 1 and a.

The Burr distribution has the following special cases:

A Loglogistic distribution when shape1 == 1;
A Paralogistic distribution when shape2 == shape1;
A Pareto distribution when shape2 == 1.

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

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

Value

dburr gives the density, pburr gives the distribution function, qburr gives the quantile function, rburr generates random deviates, mburr gives the kth raw moment, and levburr gives the kth moment of the limited loss variable.

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

Note

levburr computes the limited expected value using betaint.

Distribution also known as the Burr Type XII or Singh-Maddala distribution. See also Kleiber and Kotz (2003) for alternative names and parametrizations.

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

Author(s)

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

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(dburr(1, 2, 3, log = TRUE))
p <- (1:10)/10
pburr(qburr(p, 2, 3, 2), 2, 3, 2)

## variance
mburr(2, 2, 3, 1) - mburr(1, 2, 3, 1) ^ 2

## case with shape1 - order/shape2 > 0
levburr(10, 2, 3, 1, order = 2)

## case with shape1 - order/shape2 < 0
levburr(10, 1.5, 0.5, 1, order = 2)

actuar

Actuarial Functions and Heavy Tailed Distributions

v3.1-2

GPL (>= 2)

Authors

Vincent Goulet [cre, aut], Sébastien Auclair [ctb], Christophe Dutang [aut], Nicholas Langevin [ctb], Xavier Milhaud [ctb], Tommy Ouellet [ctb], Alexandre Parent [ctb], Mathieu Pigeon [aut], Louis-Philippe Pouliot [ctb], Jeffrey A. Ryan [aut] (Package API), Robert Gentleman [aut] (Parts of the R to C interface), Ross Ihaka [aut] (Parts of the R to C interface), R Core Team [aut] (Parts of the R to C interface), R Foundation [aut] (Parts of the R to C interface)

Initial release

2021-03-30